Page 1


The world by income

Low ($1,025 or less)

Lower middle ($1,026–$4,035)

Upper middle ($4,036–$12,475)

High ($12,476 or more)

No data

Classified according to World Bank estimates of 2011 GNI per capita

Greenland (Den)

Iceland

Norway

Faeroe Islands (Den)

Sweden

Finland

Russian Federation

The Netherlands

Estonia Denmark Russian Latvia Fed. Lithuania United Belarus Germany Poland Kingdom Belgium Ukraine Moldova Romania France Italy

Isle of Man (UK)

Canada

Ireland Channel Islands (UK)

Luxembourg Liechtenstein Switzerland Andorra United States

Bulgaria

Portugal

Spain Monaco Tunisia Algeria

Cayman Is.(UK)

Mexico

Costa Rica

Jordan

Libya

Arab Rep. of Egypt

Cape Verde

Mali

Niger

Chad

Senegal

The Gambia Guinea-Bissau R.B. de Venezuela

Guyana Suriname

Burkina Faso

Guinea

Sierra Leone Liberia

French Guiana (Fr)

Benin

Côte Ghana d’Ivoire

Nigeria Cameroon

Central African Republic

Gabon

Congo

Malawi

Zambia Bolivia Zimbabwe

Tonga

Namibia Paraguay Germany

St. Martin (Fr) St. Maarten (Neth)

Antigua and Barbuda St. Kitts and Nevis

Curaçao (Neth)

St. Vincent and the Grenadines

Dominica St. Lucia Barbados Grenada

R.B. de Venezuela

Argentina

Trinidad and Tobago

Poland

Czech Republic Ukraine Slovak Republic Austria

Guadeloupe (Fr)

Martinique (Fr) Aruba (Neth)

Chile

Uruguay

N. Mariana Islands (US) Guam (US)

Philippines

Federated States of Micronesia

Brunei Darussalam Malaysia

Marshall Islands

Palau

Maldives Nauru

Singapore

Botswana

Comoros

Solomon Islands

Papua New Guinea

Indonesia

Mayotte (Fr)

Madagascar

Tuvalu

Vanuatu

Fiji

Mauritius Réunion (Fr)

Australia

New Caledonia (Fr)

Lesotho

New Zealand

Hungary

Slovenia Croatia

Romania

Bosnia and Herzegovina San Marino Italy Montenegro Vatican City

South Africa

Kiribati

Seychelles

Mozambique

Swaziland

U.S. Virgin Islands (US)

Sri Lanka Somalia

Angola

Puerto Rico (US)

Lao P.D.R.

Timor-Leste

French Polynesia (Fr) American Samoa (US)

Myanmar

Vietnam Cambodia

Brazil

Peru

Bangladesh India

Thailand

Kenya

Rwanda Dem.Rep.of Burundi Congo Tanzania

Japan

Bhutan

Nepal

Rep. of Yemen

Ethiopia

South Sudan Uganda

Kiribati

Dominican Republic

Pakistan

United Arab Emirates Oman

Rep.of Korea

China

Afghanistan

Djibouti

Togo Equatorial Guinea São Tomé and Príncipe

Ecuador

Eritrea

Dem.People’s Rep.of Korea

Tajikistan

Bahrain Qatar Saudi Arabia

Sudan

Turkmenistan

Islamic Rep. of Iran Kuwait

Iraq

Mongolia Kyrgyz Rep.

Uzbekistan

Azerbaijan

Mauritania

Colombia

Fiji

West Bank and Gaza

Western Sahara

Haiti

Panama

Samoa

Syrian Arab Rep.

Turks and Caicos Is. (UK)

Cuba Belize Jamaica Guatemala Honduras El Salvador Nicaragua

Georgia Armenia

Cyprus Lebanon Israel

Malta

Morocco

The Bahamas

Turkey

Greece

Gibraltar (UK)

Bermuda (UK)

Kazakhstan

Serbia

Kosovo Bulgaria FYR Macedonia Albania Greece

Antarctica

IBRD 39817 MARCH 2013

Designed, edited, and produced by Communications Development Incorporated, Washington, D.C., with Peter Grundy Art & Design, London


2013

World Development Indicators


© 2013 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW, Washington DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org Some rights reserved 1 2 3 4 16 15 14 13 This work is a product of the staff of The World Bank with external contributions. Note that The World Bank does not necessarily own each component of the content included in the work. The World Bank therefore does not warrant that the use of the content contained in the work will not infringe on the rights of third parties. The risk of claims resulting from such infringement rests solely with you. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Nothing herein shall constitute or be considered to be a limitation upon or waiver of the privileges and immunities of The World Bank, all of which are specifically reserved. Rights and Permissions

This work is available under the Creative Commons Attribution 3.0 Unported license (CC BY 3.0) http://creativecommons.org/licenses/by/3.0. Under the Creative Commons Attribution license, you are free to copy, distribute, transmit, and adapt this work, including for commercial purposes, under the following conditions: Attribution—Please cite the work as follows: World Bank. 2013. World Development Indicators 2013. Washington, DC: World Bank. doi: 10.1596/978-0-8213-9824-1. License: Creative Commons Attribution CC BY 3.0 Translations—If you create a translation of this work, please add the following disclaimer along with the attribution: This translation was not created by The World Bank and should not be considered an official World Bank translation. The World Bank shall not be liable for any content or error in this translation. All queries on rights and licenses should be addressed to the Office of the Publisher, The World Bank, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@ worldbank.org. ISBN (paper): 978-0-8213-9824-1 ISBN (electronic): 978-0-8213-9825-8 DOI: 10.1596/978-0-8213-9824-1 Cover photo: Arne Hoel/World Bank; Cover design: Communications Development Incorporated. Other photos: page xviii, Arne Hoel/World Bank; page 34, Kim Eun Yeul/World Bank; page 50, Curt Carnemark/World Bank; page 64, Gerardo Pesantez/ World Bank; page 78, Maria Fleischmann/ World Bank; page 92, Curt Carnemark/World Bank.


Preface Welcome to World Development Indicators 2013, the World Bank’s premier compilation of relevant, highquality, and internationally comparable statistics about global development. The first edition of World Development Indicators in 1997 included this forecast: “The global economy is undergoing an information revolution that will be as significant in effect as the industrial revolution of the nineteenth century.” At that time the number of mobile phones worldwide was estimated to be less than 2 per 100  people, with eight times as many telephone mainlines. World Development Indicators has tracked the revolution: this edition reports that mobile phone subscriptions in 2011 grew to 85 per 100 people—a more than fortyfold increase. This is just one example of how people were communicating and acquiring knowledge and how information was changing. But in addition to measuring the change, World Development Indicators has felt it directly. Use of the online database and the tools that access it—particularly the Open Data website (http://data.worldbank.org), the web-based DataBank query application (http://databank.worldbank.org), and applications for mobile devices—has increased dramatically. And so we have refined and improved the presentation of this 17th edition. Our aim is to find the best way to put data in the hands of policymakers, development specialists, students, and the public, so that they may use the data to reduce poverty and solve the world’s most pressing development challenges. The biggest change is that the data tables previously published in the book are now available online (http://wdi.worldbank.org/tables). This has many advantages: The tables will reflect the latest

additions and revisions to the data. They will be available to a far greater audience. And they will be free for everyone. World Development Indicators 2013 is organized around six themes—world view, people, environment, economy, states and markets, and global links. Each section includes an introduction, a set of six stories highlighting regional trends, a table of the most relevant and popular indicators, and an index to the full set of tables and indicators available online. World view also reviews progress toward the Millennium Development Goals. Other companion products include The Little Data Book 2013, which provides an at-a-glance view of indicators for each economy, and a new version of the DataFinder mobile application, available in Chinese, English, French, and Spanish and designed to reflect the structure and tables of World Development Indicators 2013, for both tablet and handheld devices and for all major mobile platforms (http://data.worldbank .org/apps). World Development Indicators is the result of a collaborative effort of many partners: the United Nations family, the International Monetary Fund, the International Telecommunication Union, the Organisation for Economic Co-operation and Development, the statistical offices of more than 200 economies, and countless others. I extend my gratitude to them all—and especially to government statisticians around the world. Without their hard work, professionalism, and dedication, measuring and monitoring trends in global development would not be possible. We hope you will find the new World Development Indicators a useful resource, and we welcome any suggestions to improve it at data@worldbank.org. Shaida Badiee Director Development Economics Data Group

Economy

States and markets

Global links

Back

World Development Indicators 2013 iii


Acknowledgments This book was prepared by a team led by Soong Sup Lee under the management of Neil Fantom and comprising Azita Amjadi, Liu Cui, Federico Escaler, Mahyar Eshragh-Tabary, Juan Feng, Masako Hiraga, Wendy Ven-dee Huang, Bala Bhaskar Naidu Kalimili, Buyant Khaltarkhuu, Elysee Kiti, Alison Kwong, Ibrahim Levent, Hiroko Maeda, Johan Mistiaen, Vanessa Moreira da Silva, Maurice Nsabimana, Beatriz Prieto-Oramas, William Prince, Evis Rucaj, Rubena Sukaj, Emi Suzuki, Eric Swanson, Jomo Tariku, Rasiel Victor Vellos, and Olga Victorovna Vybornaia, working closely with other teams in the Development Economics Vice Presidency’s Development Data Group. World Development Indicators electronic products were prepared by a team led by Reza Farivari and comprising Ying Chi, Jean‑Pierre Djomalieu, Ramgopal Erabelly, Shelley Fu, Gytis Kanchas, Siddhesh Kaushik, Ugendran Machakkalai, Nacer Megherbi, Shanmugam Natarajan, Parastoo Oloumi, Manish Rathore, Ashish Shah, Atsushi Shimo, Malarvizhi Veerappan, and Vera Wen. All work was carried out under the direction of Shaida Badiee. Valuable advice was provided by

iv 

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Tito Cordella, Doerte Doemeland, Zia M. Qureshi, and David Rosenblatt. The choice of indicators and text content was shaped through close consultation with and substantial contributions from staff in the World Bank’s four thematic networks—Sustainable Development, Human Development, Poverty Reduction and Economic Management, and Financial and Private Sector Development—and staff of the International Finance Corporation and the Multilateral Investment Guarantee Agency. Most important, the team received substantial help, guidance, and data from external partners. For individual acknowledgments of contributions to the book’s content, see Credits. For a listing of our key partners, see Partners. Communications Development Incorporated provided overall design direction, editing, and layout, led by Meta de Coquereaumont, Jack Harlow, Bruce Ross-Larson, and Christopher Trott. Elaine Wilson created the cover and graphics and typeset the book. Peter Grundy, of Peter Grundy Art & Design, and Diane Broadley, of Broadley Design, designed the report. Staff from The World Bank’s Office of the Publisher oversaw printing and dissemination of the book.

World view

People

Environment


Table of contents Prefaceiii Acknowledgmentsiv Partnersvi User guide

1. World

xii

view1

2. People35 3. Environment51

Introduction Goal 1 Eradicate extreme poverty Goal 2 Achieve universal primary education Goal 3 Promote gender equality and empower women Goal 4 Reduce child mortality Goal 5 Improve maternal health Goal 6 Combat HIV/AIDS, malaria, and other diseases Goal 7 Ensure environmental sustainability Goal 8 Develop a global partnership for development Targets and indicators for each goal World view indicators About the data Online tables and indicators Poverty indicators NEW! About the data

4. Economy65 5. States

and markets79

6. Global

links93

Primary data documentation

107

Statistical methods

118

Introduction Highlights Table of indicators About the data Online tables and indicators

Credits121

Economy

States and markets

Global links

Back

World Development Indicators 2013 v


Partners Defining, gathering, and disseminating international statistics is a collective effort of many people and organizations. The indicators presented in World Development Indicators are the fruit of decades of work at many levels, from the field workers who administer censuses and household surveys to the committees and working parties of the national and international statistical agencies that develop the nomenclature, classifications, and standards fundamental to an international statistical system. Nongovernmental organizations and the private sector have also made important contributions, both in gathering primary data and in organizing and publishing their results. And academic researchers have played a crucial role in developing statistical methods and carrying on a continuing dialogue about the quality and interpreta-

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tion of statistical indicators. All these contributors have a strong belief that available, accurate data will improve the quality of public and private decisionmaking. The organizations listed here have made World Development Indicators possible by sharing their data and their expertise with us. More important, their collaboration contributes to the World Bank’s efforts, and to those of many others, to improve the quality of life of the world’s people. We acknowledge our debt and gratitude to all who have helped to build a base of comprehensive, quantitative information about the world and its people. For easy reference, web addresses are included for each listed organization. The addresses shown were active on March 1, 2013.

World view

People

Environment


International and government agencies Carbon Dioxide Information Analysis Center

International Diabetes Federation

http://cdiac.ornl.gov

www.idf.org

Centre for Research on the Epidemiology of Disasters

International Energy Agency

www.emdat.be

www.iea.org

Deutsche Gesellschaft fテシr Internationale Zusammenarbeit

International Labour Organization

www.giz.de

www.ilo.org

Food and Agriculture Organization

International Monetary Fund

www.fao.org

www.imf.org

Internal Displacement Monitoring Centre

International Telecommunication Union

www.internal-displacement.org/

www.itu.int

International Civil Aviation Organization

Joint United Programme on HIV/AIDS

www.icao.int

www.unaids.org

Economy

States and markets

Global links

Back

World Development Indicators 2013窶プii


Partners

viii 

National Science Foundation

United Nations Centre for Human Settlements, Global Urban Observatory

www.nsf.gov

www.unhabitat.org

The Office of U.S. Foreign Disaster Assistance

United Nations Children’s Fund

www.globalcorps.com/ofda.html

www.unicef.org

Organisation for Economic Co-operation and Development

United Nations Conference on Trade and Development

www.oecd.org

www.unctad.org

Stockholm International Peace Research Institute

United Nations Department of Economic and Social Affairs, Population Division

www.sipri.org

www.un.org/esa/population

Understanding Children’s Work

United Nations Department of Peacekeeping Operations

www.ucw-project.org

www.un.org/en/peacekeeping

United Nations

United Nations Educational, Scientific, and Cultural Organization, Institute for Statistics

www.un.org

www.uis.unesco.org

World Development Indicators 2013

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World view

People

Environment


United Nations Environment Programme

Upsalla Conflict Data Program

www.unep.org

www.pcr.uu.se/research/UCDP

United Nations Industrial Development Organization

World Bank

www.unido.org

http://data.worldbank.org

United Nations International Strategy for Disaster Reduction

World Health Organization

www.unisdr.org

www.who.int

United Nations Office on Drugs and Crime

World Intellectual Property Organization

www.unodc.org

www.wipo.int

United Nations Office of the High Commissioner for Refugees

World Tourism Organization

www.unhcr.org

www.unwto.org

United Nations Population Fund

World Trade Organization

www.unfpa.org

www.wto.org

Economy

States and markets

Global links

Back

World Development Indicators 2013 ix


Partners Private and nongovernmental organizations

x 

Center for International Earth Science Information Network

International Institute for Strategic Studies

www.ciesin.org

www.iiss.org

Containerisation International

International Road Federation

www.ci-online.co.uk

www.irfnet.org

DHL

Netcraft

www.dhl.com

http://news.netcraft.com

World Development Indicators 2013

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World view

People

Environment


PwC

World Economic Forum

www.pwc.com

www.weforum.org

Standard & Poor’s

World Resources Institute

www.standardandpoors.com

www.wri.org

World Conservation Monitoring Centre

www.unep-wcmc.org

Economy

States and markets

Global links

Back

World Development Indicators 2013 xi


User guide to tables World Development Indicators is the World Bank’s premier compilation of cross-country comparable data on development. The database contains more than 1,200 time series indicators for 214 economies and more than 30 country groups, with data for many indicators going back more than 50 years. The 2013 edition of World Development Indicators has been reconfigured to offer a more condensed presentation of the principal indicators, arranged in their traditional sections, along with regional and topical highlights.

3 Environment Deforestation

average annual % 2000–10

Afghanistan

 Economy

  States and markets

million metric tons

Per capita kilograms of oil equivalent

billion kilowatt hours

2010

2009

2010

2010

1,335

50

37

4.0

30

8,364

95

94

2.4

38

3.0

648

7.6

313

83

95

2.6

69

121.3

1,138

45.6

American Samoa

0.19

16.7

..

..

..

1.9

..

..

..

6.3

6.1

3,663

100

100

0.9

18

0.5

..

..

0.21

12.1

7,544

51

58

4.1

58

26.7

716

5.3

1.0

580

..

..

1.0

13

0.5

..

..

Argentina

0.81

5.3

6,771

..

..

1.0

57

174.7

1,847

125.3

Armenia

1.48

8.0

2,212

98

90

0.3

45

4.5

791

6.5

Aruba

0.00

0.0

..

100

..

0.8

..

2.3

..

..

0.37

12.5

22,039

100

100

1.3

13

400.2

5,653

241.5

–0.13

22.9

6,529

100

100

0.7

27

62.3

4,034

67.9

7.1

885

80

49.1

1,307

..

82

1.8

100

1.5

..

2.6

..

..

–3.55

0.7

3

..

..

4.9

44

24.2

7,754

13.2

Bangladesh

0.18

1.6

698

81

56

3.0

115

51.0

209

42.3

Barbados

0.00

0.1

292

100

100

1.4

35

1.6

..

0.00 0.00

1.0

58

27

World Development Indicators 2013

7.2

3,927

100

93

0.4

6

60.3

2,922

34.9

–0.16

13.2

1,089

100

100

1.2

21

103.6

5,586

93.8

0.67

20.6

44,868

98

90

3.0

12

0.4

..

..

13

4.2

0.2

Benin

48

4.9

413

0.00

5.1

..

..

..

0.7

..

0.5

..

Bhutan

–0.34

28.3

105,653

96

44

3.9

20

0.4

..

Bolivia

0.50

18.5

30,085

88

27

2.2

57

14.5

737

6.9

Bosnia and Herzegovina

0.00

0.6

9,461

99

95

0.9

21

30.1

1,703

17.1

Botswana

0.99

30.9

1,182

96

62

2.2

64

4.4

1,128

0.5 515.7

Burkina Faso

Front

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1.04

23.3

1,132

75

98

79

.. ..

0.50

26.0

27,551

1.2

18

367.1

1,363

0.44

29.6

20,939

..

..

2.2

44

9.3

8,308

3.9

–1.53

8.9

2,858

100

100

–1.7

40

42.8

2,370

46.0

1.01

14.2

737

79

17

6.2

65

1.7

..

46

1.40

4.8

1,173

72

24

0.2

Cambodia

1.34

23.4

8,431

64

31

2.1

42

4.6

355

1.0

1.05

9.0

13,629

77

49

3.3

59

6.7

363

5.9

0.00

6.2

82,647

100

100

1.2

15

513.9

7,380

607.8 ..

Canada Cape Verde

599

88

61

0.3

..

0.00

1.5

..

96

96

..

0.5

..

0.13

17.7

31,425

67

34

2.6

35

0.2

..

0.66

9.4

1,301

51

13

3.0

83

0.4

..

..

..

0.5

..

..

..

0.8

..

..

..

..

Chile

–0.25

13.3

51,188

96

96

1.1

46

66.7

1,807

60.4

China

–1.57

16.0

2,093

91

64

3.0

59

7,687.1

1,807

4,208.3

37.0

1,951

38.3

Macao SAR, China

..

41.8

..

..

2.1 0.9

..

0.1

..

2.2

..

..

Chad

Hong Kong SAR, China

0.2

..

Central African Republic Channel Islands

–0.36

4.9

..

Burundi Cameroon

Cayman Islands

..

.. ..

..

..

..

..

..

1.5

..

..

Colombia

0.17

20.5

45,006

92

77

1.7

19

71.2

696

56.8

Comoros

9.34

..

1,592

95

36

2.9

30

0.1

..

..

Congo, Dem. Rep.

0.20

10.0

13,283

45

24

4.3

35

2.7

360

7.9

Congo, Rep.

0.07

9.7

53,626

71

18

3.0

57

1.9

363

0.6

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World Development Indicators 2013

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World view

People

Data presentation conventions • A blank means not applicable or, for an aggregate, not analytically meaningful. • A billion is 1,000 million. • A trillion is 1,000 billion. • Figures in orange italics refer to years or periods other than those specified or to growth rates calculated for less than the full period specified. • Data for years that are more than three years from the range shown are footnoted. • The cutoff date for data is February 1, 2013.

World view

..

–0.43

Belgium

Aggregate measures for income groups

xii 

18.7

Belarus

Bulgaria

The aggregate measures for regions cover only low- and middle-income economies. The country composition of regions is based on the World Bank’s analytical regions and may differ from common geographic usage. For regional classifications, see the map on the inside back cover and the list on the back cover flap. For further discussion of aggregation methods, see Statistical methods.

..

0.00 0.20

Brunei Darussalam

Aggregate measures for regions

..

Antigua and Barbuda

Brazil

Aggregate measures for income groups include the 214 economies listed in the tables, plus Taiwan, China, whenever data are available. To maintain consistency in the aggregate measures over time and between tables, missing data are imputed where possible.

..

Angola

Belize

46

1990–2011

0.4

Bermuda

The tables include all World Bank member countries (188), and all other economies with populations of more than 30,000 (214 total). Countries and economies are listed alphabetically (except for Hong Kong SAR, China, and Macao SAR, China, which appear after China). The term country, used interchangeably with economy, does not imply political independence but refers to any territory for which authorities report separate social or economic statistics. When available, aggregate measures for income and regional groups appear at the end of each table.

2010

8.4

Bahamas, The

Tables

% of total population

2010

Energy use Electricity production

weighted PM10 micrograms per cubic meter

6.2

Bahrain

  Global links

% of total population

average annual % growth

0.57

Azerbaijan

 Environment

2011

Access to Urban Particulate Carbon improved population matter dioxide sanitation concentration emissions facilities urban-population-

–0.10

Austria

 People

2011

Access to improved water source

Albania

Australia

  World view

Internal renewable freshwater

Algeria Andorra

0.00

Nationally protected areas

b Terrestrial and resources marine areas Per capita % of total territorial area cubic meters

People

Environment

Environment


Classification of economies For operational and analytical purposes the World Bank’s main criterion for classifying economies is gross national income (GNI) per capita (calculated using the World Bank Atlas method). Because GNI per capita changes over time, the country composition of income groups may change from one edition of World Development Indicators to the next. Once the classification is fixed for an edition, based on GNI per capita in the most recent year for which data are available (2011 in this edition), all historical data presented are based on the same country grouping. Low-income economies are those with a GNI per capita of $1,025 or less in 2011. Middle-income economies are those with a GNI per capita of more than $1,025 but less than $12,475. Lower middle-income and upper middleincome economies are separated at a GNI per capita of $4,036. High-income economies are those with a GNI per capita of $12,476 or more. The 17 participating member countries of the euro area are presented as a subgroup under high income economies.

Environment 3 Deforestation

average annual % 2000–10

Nationally protected areas

Internal renewable freshwater b Terrestrial and resources marine areas Per capita % of total territorial area cubic meters 2011

2011

Access to improved water source

Access to Urban Particulate Carbon improved population matter dioxide sanitation concentration emissions facilities urban-population-

% of total population

% of total population

average annual % growth

2010

2010

1990–2011

Energy use Electricity production

weighted PM10 micrograms per cubic meter

million metric tons

Per capita kilograms of oil equivalent

billion kilowatt hours

2010

2009

2010

2010

Costa Rica

–0.93

17.6

23,780

97

95

2.2

27

Côte d’Ivoire

–0.15

21.8

3,813

80

24

3.5

30

6.6

485

6.0

Croatia

–0.19

9.5

8,562

99

99

0.2

22

21.5

1,932

14.0

Cuba

–1.66

5.3

3,387

94

91

–0.1

15

31.6

975

17.4

Curacao

..

..

..

998

9.6

..

..

..

..

..

Cyprus

–0.09

4.5

699

100

100

1.4

27

8.2

2,215

Czech Republic

–0.08

15.1

1,253

100

98

–0.3

16

108.1

4,193

85.3

Denmark

–1.14

4.1

1,077

100

100

0.6

15

45.7

3,470

38.8

331

88

50

0.00

2.0

28

Dominica

0.58

3.7

..

..

..

0.1

20

0.1

..

..

Dominican Republic

0.00

24.1

2,088

86

83

2.1

14

20.3

840

15.9

Ecuador

1.81

38.0

29,456

94

92

2.2

19

30.1

836

17.7 146.8

–1.73

..

..

22

99

95

2.1

78

216.1

903

1.45

1.4

2,850

88

87

1.3

28

6.3

677

Equatorial Guinea

0.69

14.0

36,100

..

..

3.2

6

4.8

..

Eritrea

0.28

3.8

517

61

14

5.2

61

0.5

142

0.3

Estonia

0.12

22.6

9,486

98

95

0.1

9

16.0

4,155

13.0

Ethiopia

1.08

18.4

1,440

44

21

3.7

47

7.9

400

5.0

El Salvador

Faeroe Islands

0.00

Fiji

–0.34

6.1

0.5

.. 5.4

Djibouti

Egypt, Arab Rep.

0.0

..

8.3

..

..

..

..

0.8

100

100

0.6

15

53.6

6,787

80.7

3,057

100

100

1.2

12

363.4

4,031

564.3

French Polynesia

–3.97

0.1

..

100

98

1.1

..

0.9

..

..

14.6

106,892

33

2.3

7

87

68

1.6

1,418 ..

..

1.8

1.3

1,689

3.7

60

Georgia

0.09

3.4

12,958

98

95

1.0

49

5.8

700

10.1

Germany

0.00

42.3

1,308

100

100

0.2

16

734.6

4,003

622.1

Ghana

2.08

14.0

1,214

86

14

3.6

22

7.4

382

8.4

Greece

27

57.4

0.3

0.4

..

..

19,858

89

0.8

..

8.5 17.1

0.00

20

0.7

32,876

0.14 –0.39

–0.41

1.7

11

0.2

Finland

Gabon

83

..

France

Gambia, The

98

6.0

–0.81

9.9

5,133

100

98

94.9

2,440

Greenland

0.00

40.1

..

100

100

0.2

..

0.6

..

Grenada

0.00

0.1

..

..

97

1.3

19

0.2

..

..

.. ..

Guam

0.00

3.6

..

100

99

1.3

..

..

..

..

Guatemala

1.40

29.5

7,400

92

78

3.4

51

15.2

713

8.8

Guinea

0.54

6.4

22,110

74

18

3.8

55

1.2

..

..

Guinea-Bissau

0.48

26.9

10,342

64

20

3.6

48

0.3

..

..

4.8

318,766

Guyana

0.00

Haiti

94

0.5

17

3.8

35

77

3.1

34

7.7

601

6.7

0.4

15

48.7

2,567

37.4

16,882

0.1

1,285

13.9

12,371

87

–0.62

5.1

602

100

100

Iceland

–4.99

13.2

532,892

100

100

India

–0.46

Honduras

69

84

0.76 2.06

Hungary

1.6 2.3

.. 229

.. 0.6

18

2.0

4.8

1,165

2.5

52

1,979.4

Indonesia

0.51

6.4

8,332

82

54

2.5

60

451.8

867

169.8

Iran, Islamic Rep.

0.00

6.9

1,718

96

100

1.3

56

602.1

2,817

233.0

73

2.8

88

109.0

1,180

50.2

2.7

13

41.6

Iraq

–0.09

Ireland

–1.53

Isle of Man Israel

Economy

0.1

1,068

1.2

10,707

92

34

79 100

99

0.4

20

566

3,218

17.1 959.9

..

..

..

..

0.5

..

..

..

..

15.1

97

100

100

1.9

21

67.2

3,005

58.6

States and markets

Global links

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Statistics

28.4

0.00 –0.07

World Development Indicators 2013

47

Additional information about the data is provided in Primary data documentation, which summarizes national and international efforts to improve basic data collection and gives country-level information on primary sources, census years, fiscal years, statistical methods and concepts used, and other background information. Statistical methods provides technical information on some of the general calculations and formulas used throughout the book.

Symbols

Country notes

..

• Data for China do not include data for Hong Kong SAR, China; Macao SAR, China; or Taiwan, China. • Data for Indonesia include Timor-Leste through 1999. • Data for Mayotte, to which a reference appeared in previous editions, are included in data for France. • Data for Serbia do not include data for Kosovo or Monte­negro. • Data for Sudan include South Sudan unless otherwise noted.

means that data are not available or that aggregates cannot be calculated because of missing data in the years shown.

0 or means zero or small enough that the number would 0.0 round to zero at the displayed number of decimal places. /

in dates, as in 2010/11, means that the period of time, usually 12 months, straddles two calendar years and refers to a crop year, a survey year, or a fiscal year.

$

means current U.S. dollars unless otherwise noted.

<

means less than.

Economy

States and markets

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World Development Indicators 2013 xiii


User guide to WDI online tables Statistical tables that were previously available in the World Development Indicators print edition are now available online. Using an automated query process, these reference tables will be consistently updated based on the revisions to the World Development Indicators database.

How to access WDI online tables To access the WDI online tables, visit http://wdi.worldbank .org/tables. To access a specific WDI online table directly,

xivâ&#x20AC;&#x192;

World Development Indicators 2013

Front

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User guide

use the URL http://wdi.worldbank.org/table/ and the table number (for example, http://wdi.worldbank.org/ table/1.1 to view the first table in the World view section). Each section of this book also lists the indicators included by table and by code. To view a specific indicator online, use the URL http://data.worldbank.org/ indicator/ and the indicator code (for example, http://data .worldbank.org/indicator/SP.POP.TOTL to view a page for total population).

World view

People

Environment


Breadcrumbs to show where youâ&#x20AC;&#x2122;ve been

Click on an indicator to view metadata

Click on a country to view metadata

How to use DataBank

Actions

DataBank (http://databank.worldbank.org) is an online web resource that provides simple and quick access to collections of time series data. It has advanced functions for selecting and displaying data, performing customized queries, downloading data, and creating charts and maps. Users can create dynamic custom reports based on their selection of countries, indicators, and years. All these reports can be easily edited, shared, and embedded as widgets on websites or blogs. For more information, see http://databank.worldbank.org/help.

Click to edit and revise the table in DataBank Click to print the table and corresponding indicator metadata Click to export the table to Excel Click to export the table and corresponding indicator metadata to PDF Click to access the WDI Online Tables Help file Click the checkbox to highlight cell level metadata and values from years other than those specified; click the checkbox again to reset to the default display

Economy

States and markets

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World Development Indicators 2013â&#x20AC;&#x192;xv


User guide to DataFinder

xvi 

DataFinder is a free mobile app that accesses the full set of data from the World Development Indicators database. Data can be displayed and saved in a table, chart, or map and shared via email, Facebook, and Twitter.

DataFinder works on mobile devices (smartphone or tablet computer) in both offline (no Internet connection) and online (Wi-Fi or 3G/4G connection to the Internet) modes.

• • • •

• View reports in table, chart, and map formats. • Send the data as a CSV file attachment to an email. • Share comments and screenshots via Facebook, ­Twitter, or email.

Select a topic to display all related indicators. Compare data for multiple countries. Select predefined queries. Create a new query that can be saved and edited later.

World Development Indicators 2013

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User guide

World view

People

Environment


Table view provides time series data tables of key development indicators by country or topic. A compare option shows the most recent yearâ&#x20AC;&#x2122;s data for the selected country and another country.

Chart view illustrates data trends and cross-country comparisons as line or bar charts.

Map view colors selected indicators on world and regional maps. A motion option animates the data changes from year to year.

Economy

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World Development Indicators 2013â&#x20AC;&#x192;xvii


WORLD VIEW xviiiâ&#x20AC;&#x192;

World Development Indicators 2013

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User guide

World view People Environment


The Millennium Declaration adopted by all the members of the United Nations General Assembly in 2000 represents a commitment to a more effective, results-oriented development partnership in the 21st century. Progress documented here and in the annual reports of the United Nations Secretary-­General has been encouraging: poverty rates have fallen, more children— especially girls—are enrolled in and completing school, and they are—on average—living longer and healthier lives. Fewer mothers die in child birth, and more women have access to reproductive health services. The indicators used to monitor the Millennium Development Goals have traced the path of the HIV epidemic, the resurgence and retreat of tuberculosis, and the step-by-step efforts to “roll back malaria.” More people now have access to reliable water supplies and basic sanitation facilities. But forests continue to disappear and with them the habitat for many species of plants and animals, and greenhouse gases continue to accumulate in the atmosphere. From the start monitoring the Millennium Development Goals posed three challenges: selecting appropriate targets and indicators, constructing an international database for global monitoring, and significantly improving the quality, frequency, and availability of the relevant statistics. When they were adopted, the target year of 2015 seemed comfortably far away, and the baseline year of 1990 for measuring progress seemed a reasonable starting point with wellestablished data. As we near the end of that 25-year span, we have a better appreciation of how great those challenges were. Already there is discussion of the post2015 development agenda and the monitoring framework needed to record commitments and Economy

States and markets

measure progress. The Millennium Development Goals have contributed to the development of a statistical infrastructure that is increasingly capable of producing reliable statistics on various topics. The post-2015 agenda and a welldesigned monitoring framework will build on that infrastructure. The international database for monitoring the Millennium Development Goals is a valuable resource for analyzing many development issues. The effort of building and maintaining such a database should not be underestimated, and it will take several years to implement a new framework of goals and targets. To serve as an analytical resource, the database will need to include additional indicators, beyond those directly associated with the targets and the core data for conducting these indicators. New technologies and methods for reporting data should improve the quality and timeliness of the resulting database. The quality of data will ultimately depend on the capacity of national statistical systems, where most data originate. When the Millennium Development Goals were adopted, few developing countries had the capacity or resources to produce statistics of the requisite quality or frequency. Despite much progress, the statistical capacity-building programs initiated over the last decade should continue, and other statistical domains need attention. Planning for post-2015 goals must include concomitant plans for investments in statistics—by governments and development partners alike. The effort to achieve the Millennium Development Goals has been enormous. The next set of goals will require an even larger effort. Without good statistics, we will never know if we have succeeded. Global links

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World Development Indicators 2013 1


Goal 1 Eradicate extreme poverty Poverty rates continue to fall

1a

People living on less than 2005 PPP $1.25 a day (%) 75

Forecast 2010–15

Sub-Saharan Africa 50 South Asia

25 Europe & Central Asia

Latin America & Caribbean Middle East & North Africa 0

1990

1995

2000

2005

2010 estimate

2015 forecast

Source: World Bank PovcalNet.

Progress in reaching the poverty target, 1990–2010

1b

Share of countries making progress toward reducing poverty (%) 100

50

0

50 Reached target 100

On track

Off track

Seriously off track

East Asia

Europe Latin Middle East & Central America & & North Asia Caribbean Africa Source: World Bank staff calculations.

South Asia

Sub-Saharan Africa

Fewer people are living in extreme poverty

1c

People living on less than 2005 PPP $1.25 a day (billions) 2.0

Forecast 2010–15

Europe & Central Asia Latin America & Caribbean

1.5 Middle East & North Africa

1.0

0.5

0.0

South Asia Sub-Saharan Africa 1990

1995

2000

2005

2010 estimate

2015 forecast

Source: World Bank PovcalNet.

2 

World Development Indicators 2013

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User guide

The world will not have eradicated extreme poverty in 2015, but the Millennium Development Goal target of halving world poverty will have been met. The proportion of people living on less than $1.25 a day fell from 43.1 percent in 1990 to 22.7 percent in 2008, reaching new lows in all six developing country regions. While the food, fuel, and financial crises over the past five years worsened the situation of vulnerable populations and slowed poverty reduction in some countries, global poverty rates continued to fall in most regions. Preliminary estimates for 2010 confirm that the extreme poverty rate fell further, to 20.6 percent, reaching the global target five years early. Except in South Asia and Sub-­ Saharan Africa the target has also been met at the regional level (figure 1a). Fur ther progress is possible and likely before the 2015 target date of the Millennium Development Goals. Developing economies are expected to maintain GDP growth of 6.6–6.8 percent over the next three years, with growth of GDP per capita around 5.5 percent. Growth will be fastest in East Asia and Pacific and South Asia, which still contain more than half the world’s poorest people. Growth will be slower in Sub-­S aharan Africa, the poorest region in the world, but faster than in the preceding years, quickening the pace of poverty reduction. According to these forecasts, the proportion of people living in extreme poverty will fall to 16 percent by 2015. Based on current trends, 59 of 112 economies with adequate data are likely to achieve the first Millennium Development Goal (figure 1b). The number of people living in extreme poverty will continue to fall to less than a billion in 2015 (figure 1c). Of these, 40 percent will live in South Asia and 40 percent in Sub-­S aharan Africa. How fast poverty reduction will proceed depends not just on the growth of GDP but also on its distribution. Income distribution has improved in some countries, such as Brazil, while

World view People Environment


worsening in others, such as China. To speed progress toward eliminating extreme poverty, development strategies should attempt to increase not just the mean rate of growth but also the share of income going to the poorest part of the population. Sub-­Saharan Africa, where average income is low and average income of those below the poverty line is even lower, will face great difficulties in bringing the poorest people to an adequate standard of living (figure 1d). Latin America and the Caribbean, where average income is higher, must overcome extremely inequitable income distributions. Two Millennium Development Goal indicators address hunger and malnutrition. Child malnutrition, measured by comparing a child’s weight with that of other children of similar age, reflects a shortfall in food energy, poor feeding practices by mothers, and lack of essential nutrients in the diet. Malnutrition in children often begins at birth, when poorly nourished mothers give birth to underweight babies. Malnourished children develop more slowly, enter school later, and perform less well. Malnutrition rates have dropped substantially since 1990, from 28 percent of children under age 5 in developing countries to 17 percent in 2011. Every developing region except Sub-­S aharan Africa is on track to cut child malnutrition rates in half by 2015 (figure 1e). However, collecting data on malnutrition through surveys with direct measurement of children’s weight and height is costly, and many countries lack the information to calculate time trends. Undernourishment, a shortage of food energy to sustain normal daily activities, is affected by changes in the average amount of food available and its distribution. After steady declines in most regions from 1991 to 2005, further improvements in undernourishment have stalled, leaving 13 percent of the world’s population, almost 900 million people, without adequate daily food intake (figure 1f).

Economy

States and markets

Poorer than poor

1d

Average daily income of people living on less than 2005 PPP $1.25 a day, 2008 (2005 PPP $) 1.25

1.00

0.75

0.50

0.25

0.00

East Asia & Pacific

Europe & Central Asia

Latin America & Caribbean

Middle East & North Africa

South Asia

Sub-Saharan Africa

Source: World Bank PovcalNet.

Fewer malnourished children

1e

Malnutrition prevalence, weight for age (% of children under age 5) 60 South Asia

40 Sub-Saharan Africa

20

East Asia & Pacific Middle East & North Africa Europe & Central Asia Latin America & Caribbean

0

1990

1995

2000

2005

2011

Source: World Development Indicators database.

And fewer people lacking sufficient food energy

1f

Undernourishment prevalence (% of population) 40

30 Sub-Saharan Africa South Asia 20

Latin America & Caribbean

10

East Asia & Pacific

Middle East & North Africa Europe & Central Asia

0

1991

1996

2001

2006

2011

Source: Food and Agriculture Organization and World Development Indicators database.

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World Development Indicators 2013 3


Goal 2 Achieve universal primary education Growth in complete primary education has slowed

2a

Primary school completion rate (% of relevant age group) 125

East Asia & Pacific

Latin America & Caribbean

Europe & Central Asia 100

South Asia

75

Middle East & North Africa

Sub-Saharan Africa

50 25

0

1990

1995

2000

2005

2010

2015

Note: Dotted lines indicate progress needed to reach target. Source: United Nations Educational, Scientific and Cultural Organization Institute of Statistics and World Development Indicators database.

Progress toward universal primary education, 1990–2010

2b

Share of countries making progress toward universal primary education (%) 100

50

0

50

100

Reached target Insufficient data

On track

Off track

Seriously off track

East Asia & Pacific

Europe Latin Middle East & Central America & & North Asia Caribbean Africa Source: World Bank staff calculations.

South Asia

Sub-Saharan Africa

More girls than boys remain out of school in most regions

2c

Children not attending primary school, 2010 (% of relevant age group) 30 25 20 15

0

Girls

5

Boys

10

Europe Latin Middle East South Sub-Saharan & Central America & & North Asia Africa Asia Caribbean Africa Source: United Nations Educational, Scientific and Cultural Organization Institute of Statistics and World Development Indicators database.

4 

World Development Indicators 2013

East Asia & Pacific

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User guide

The commitment to provide primary education to every child is the oldest of the Millennium Development Goals, having been set at the first Education for All conference in Jomtien, Thailand, more than 20 years ago. Progress among the poorest countries has accelerated since 2000, particularly in South Asia and Sub-­Saharan Africa, but full enrollment remains elusive. Many children start school but drop out before completion, discouraged by cost, distance, physical danger, and failure to progress. Even as countries approach the target, the education demands of modern economies expand, and primary education will increasingly be of value only as a stepping stone toward secondary and higher education. In most developing country regions school enrollment picked up after the Millennium Development Goals were promulgated in 2000, when the completion rate was 80 percent. Sub-­ Saharan Africa and South Asia, which started out farthest behind, made substantial progress. By 2009 nearly 90 percent of children in developing countries completed primary school, but completion rates have stalled since, with no appreciable gains in any region (figure  2a). Three regions have attained or are close to attaining complete primary education: East Asia and Pacific, Europe and Central Asia, and Latin America and the Caribbean (figure 2b). Completion rates in the Middle East and North Africa have stayed at 90 percent since 2008. South Asia has reached 88 percent, but progress has been slow. And Sub-­S aharan Africa lags behind at 70 percent. Even if the schools in these regions were to now enroll every eligible child in the first grade, they would not be able to achieve a full course of primary education by 2015. But it would help. Many children enroll in primary school but attend intermittently or drop out entirely. This is particularly true for girls—almost all school

World view People Environment


systems with low enrollment rates show under­ enrollment of girls in primary school, since their work is needed at home (figure 2c). Other obstacles discourage parents from sending their children to school, including the need for boys and girls during planting and harvest, lack of suitable school facilities, absence of teachers, and school fees. The problem is worst in South Asia and Sub-­Saharan Africa, where more than 46 million children of primary school age are not in school. Not all children have the same opportunities to enroll in school or remain in school. Across the world, children in rural areas are less likely to enter school, and when they do, they are likely to drop out sooner. In Ethiopia nearly all urban children complete first grade, but fewer than 80  percent of rural children do (figure  2d). In Senegal, where slightly more than 80 percent of urban children complete first grade, barely half of rural children begin the first grade and only 40  percent remain after nine years. Cambodia follows a similar pattern. Parents’ education makes a big difference in how far children go in school. In Nepal, for example, less than 90 percent of children whose parents lack any education complete first grade and barely 70 percent remain through the ninth (figure  2e). But 95  percent of children from households with some higher education stay through nine grades, and many of those go onto complete secondary school and enter tertiary education. Income inequality and educational quality are closely linked. Take Senegal (figure 2f). Children from wealthier households (as measured by a household’s ownership of certain assets) are more likely to enroll and stay in school than children from poorer households. Thus children from poor households are least likely to acquire the one asset—human capital—that could most help them to escape poverty.

Rural students at a disadvantage

2d

Share of people ages 10–19 completing each grade of schooling, by location, 2010–11 (%) 100

Urban Cambodia Urban Senegal

Urban Ethiopia

75 Rural Ethiopia

50

Rural Cambodia

Rural Senegal

25

0

Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Grade 6 Grade 7 Grade 8 Grade 9

Source: Demographic and Health Surveys and World Bank EdStats database.

Parents’ education makes a difference in Nepal

2e

Share of people ages 10–19 completing each grade of schooling, by parents’ education level, 2011 (%) Some higher

100

Incomplete primary

Primary

75

Incomplete secondary No education

50

25

0

Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Grade 6 Grade 7 Grade 8 Grade 9

Source: Demographic and Health Surveys and World Bank EdStats database.

Poverty is a barrier to education in Senegal

2f

Share of people ages 10–19 completing each grade of schooling, by wealth quintile, 2010 (%) 100 Wealthiest quintile 75

Fourth quintile Third quintile

50 Second quintile Poorest quintile

25

0

Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Grade 6 Grade 7 Grade 8 Grade 9

Source: Demographic and Health Surveys and World Bank EdStats database.

Economy

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World Development Indicators 2013 5


Goal 3 Promote gender equality and empower women A ragged kind of parity in school enrollments

3a

Ratio of girls’ to boys’ gross enrollment, 2011 (%)

Primary

East Asia & Pacific

Secondary Tertiary

Europe & Central Asia

Latin America & Caribbean

Middle East & North Africa

South Asia

Sub-Saharan Africa

0

25

50

75

100

125

Source: United Nations Educational, Scientific and Cultural Organization Institute of Statistics and World Development Indicators database.

Progress toward gender equality in education, 1990–2010

3b

Share of countries making progress toward gender equality in primary and secondary education (%) 100

50

0

50

100

Reached target Insufficient data

On track

Off track

Seriously off track

Europe Latin Middle East & Central America & & North Asia Caribbean Africa Source: World Bank staff calculations.

6 

World Development Indicators 2013

East Asia & Pacific

Front

South Asia

?

Sub-Saharan Africa

User guide

Women make important contributions to economic and social development. Expanding opportunities for them in the public and private sectors is a core development strategy, and education is the starting point. By enrolling and staying in school, girls gains skills needed to enter the labor market, care for families, and make decisions for themselves. Achieving gender equality in education is an important demonstration that young women are full, contributing members of society. Girls have made substantial gains in school enrollment. In 1990 girls’ primary school enrollment rate in developing countries was only 86 percent of boys’. By 2011 it was 97 percent (figure 3a). Similar improvements have been made in secondary schooling, where girls’ enrollments have risen from 78 percent of boys’ to 96 percent over the same period. But the averages mask large differences across countries. At the end of 2011, 31 upper middle-income countries had reached or exceeded equal enrollment of girls in primary and secondary education, as had 23  lower middle-income countries but only 9 low-income countries. South Asia and Sub-­ Saharan Africa are lagging behind (figure 3b). Patterns of school attendance at the national level mirror those at the regional level: poor households are less likely than wealthy households to keep their children in school, and girls from wealthier households are more likely to enroll in school and stay longer. Ethiopia is just one example of the prevailing pattern documented by household surveys from many developing countries (figure 3c). More women are participating in public life at the highest levels. The proportion of parliamentary seats held by women continues to increase. In Latin America and the Caribbean women now hold 25 percent of all parliamentary seats (figure 3d). The most impressive gains have been made in the Middle East and North Africa, where the proportion of seats held by women more than tripled between 1990 and 2012. Algeria leads the way with 32 percent. In Nepal a third of parliamentary

World view People Environment


seats were held by women in 2012. Rwanda continues to lead the world. Since 2008, 56 percent of parliamentary seats have been held by women. Women work long hours and make important contributions to their families’ welfare, but many in the informal sector are unpaid for their labor. The largest proportion of women working in the formal sector is in Europe and Central Asia, where the median proportion of women in wage employment outside the agricultural sector was 46 percent (figure  3e). Latin America and the Caribbean is not far behind, with 42 percent of women in nonagricultural employment. Women’s share in paid employment in the nonagricultural sector has risen marginally but remains less than 20 percent in most countries in the Middle East and North Africa and South Asia and less than 35 percent in Sub-­Saharan Africa. In these regions full economic empowerment of women remains a distant goal. Lack of data hampers the ability to understand women’s roles in the economy. The Evidence and Data for Gender Equality (EDGE) Initiative is a new partnership, jointly managed by UN Women and the United Nations Statistics Division, in collaboration with member states, the World Bank, the Organisation for Economic Co-operation and Development, and others, that seeks to accelerate the work of gathering indicators on women’s education, employment, entrepreneurship, and asset ownership. During its initial phase EDGE will lay the ground work for a database of basic education and employment indicators, developing standards and guidelines for entrepreneurship and assets indicators, and pilot data in several countries. Relevant indicators could include the percentage distribution of the employed population, by sector and sex; the proportion of employed who are employer, by sex; the length of maternity leave; the percentage of firms owned by women; the proportion of the population with access to credit, by sex; and the proportion of the population who own land, by sex.

Economy

States and markets

Girls are disadvantaged at every income level in Ethiopia

3c

Share of people ages 10–19 completing each grade of schooling, by sex and wealth quintile, 2011 (%) 100

Wealthiest quintile, boys Wealthiest quintile, girls Third quintile, boys

75

Third quintile, girls

50 Poorest quintile, boys Poorest quintile, girls

25

0

Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Grade 6 Grade 7 Grade 8 Grade 9

Source: Demographic and Health Surveys and World Bank EdStats database.

More women in parliaments

3d

Share of seats held by women in national parliaments (%) 25

20 East Asia & Pacific Latin America & Caribbean

15

Europe & Central Asia

Sub-Saharan Africa

10

South Asia 5

0

Middle East & North Africa 1990

1995

2000

2005

2012

Source: Inter-Parliamentary Union and World Development Indicators database.

Women still lack opportunities in the labor market

3e

Share of women employed in the nonagricultural sector, median value, most recent year available, 2004–10 (% of total nonagricultural employment) 50 40 30 20 10 0

East Asia & Pacifica

Europe Latin Middle East South Sub-Saharan & Central America & & North Asia Africaa Asia Caribbean Africa a. Data cover less than two-thirds of regional population. Source: International Labour Organization and World Development Indicators database.

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World Development Indicators 2013 7


Goal 4 Reduce child mortality Under-five mortality rates continue to fall

4a

Under-five mortality rate (per 1,000 live births) 200

150 Sub-Saharan Africa 100 East Asia & Pacific

South Asia

50

Middle East & North Africa

Europe & Central Asia 0

1990

Latin America & Caribbean 1995

2000

2005

2011

Source: Inter-agency Group for Child Mortality Estimation and World Development Indicators database.

Progress toward reducing child mortality, 1990–2010

4b

Share of countries making progress toward reducing child mortality (%) 100

50

0

50

100

Reached target Insufficient data

On track

Off track

Seriously off track

East Asia & Pacific

Europe Latin Middle East & Central America & & North Asia Caribbean Africa Source: World Bank staff calculations.

South Asia

Sub-Saharan Africa

As mortality rates fall, a larger proportion of deaths occur in the first month

4c

Deaths of children under age 5, 2011 (millions) 4

Children (ages 1–4) Infants (ages 1–11 months) Neonatals (ages 0–1 month)

3

2

1

0

Europe Latin Middle East South Sub-Saharan & Central America & & North Asia Africa Asia Caribbean Africa Source: Inter-agency Group for Child Mortality Estimation and World Development Indicators database.

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In 1990, 12 million children died before their fifth birthday. By 1999 fewer than 10 million did. And in 2012 7 million did. In developing countries the under-five mortality rate fell from an average of 95 per 1,000 live births in 1990 to 56 in 2011, but rates in Sub-­Saharan Africa and South Asia remain much higher (figure 4a). Currently, 41 countries are poised to reach the Millennium Development Goal target of a two-thirds reduction in under-five mortality rates by 2015 (figure 4b). Faster improvements over the last decade suggest that many countries are accelerating progress and another 25 could reach the target as soon as 2020. Looking past 2015, still faster progress is possible if high mortality countries give priority to addressing the causes of child mortality. Concomitant reductions in fertility rates, particularly among adolescents, will also help. Most children die from causes that are readily preventable or curable with existing interventions, such as pneumonia (18 percent), diarrhea (11 percent), and malaria (7 percent). Almost 70 percent of deaths of children under age 5 occur in the first year of life, and 60 percent of those in the first month (figure 4c). Preterm birth complications account for 14 percent of deaths, and complications during birth another 9 percent (UN Inter-Agency Group for Child Mortality Estimation 2012). Therefore reducing child mortality requires addressing the causes of neonatal and infant deaths: inadequate care at birth and afterward, malnutrition, poor sanitation, and exposure to acute and chronic disease. Lower infant and child mortality rates are, in turn, the largest contributors to higher life expectancy in most countries. Childhood vaccinations are a proven, cost-­ effective way of reducing childhood illness and death. But despite years of vaccination campaigns, many children in low- and lower middle-income economies remain unprotected. To be successful, vaccination campaigns must reach all children and be sustained over time. Thus it is worrisome that measles vaccination rates in the two highest

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mortality regions, South Asia and Sub-­Saharan Africa, have stagnated in the last three years, at less than 80 percent coverage (figure 4d). Twenty countries in the developing world accounted for 4.5 million deaths among children under age 5 in 2011, or 65 percent of all such deaths worldwide (figure 4e). These countries are mostly large, often with high birth rates, but many have substantially reduced mortality rates over the past two decades. Of the 20, 11 have reached or are likely to achieve a two-thirds reduction in their under-five mortality rate by 2015: Bangladesh, Brazil, China, the Arab Republic of Egypt, Ethiopia, Indonesia, Madagascar, Malawi, Mexico, Niger, and Turkey. Had the mortality rates of 1990 prevailed in 2011, these 11 countries would have experienced 2 million more deaths. The remaining nine, where progress has been slower, have nevertheless averted 3 million deaths. If India were on track to reach the target, another 440,000 deaths would have been averted. The data used to monitor child mortality are produced by the Inter-agency Group for Child Mortality Estimation (IGME), which evaluates data from existing sources and then fits a statistical model to data points that are judged to be reliable. The model produces a trend line for under-five mortality rates in each country. Infant mortality and neonatal mortality rates are derived from under-five mortality estimates. The data come from household surveys and, where available, vital registration systems. But surveys are slow and costly. While they remain important tools for investigating certain complex, micro-level problems, vital registration systems are usually better sources of timely statistics. Recent IGME estimates of under-five mortality include new data from vital registration systems for about 70 countries. But many countries lack complete reporting of vital events, and even those that do often misreport cause of death. Vital registration supplemented by surveys and censuses offers the best approach for improving knowledge of morbidity and mortality in all age groups.

Economy

States and markets

Measles immunization rates are stagnating

4d

Children ages 12–23 months immunized against measles (%) 100

Europe & Central Asia

East Asia & Pacific

Middle East & North Africa 75

Latin America & Caribbean

South Asia

Sub-Saharan Africa

50

25

0

1990

1995

2000

2005

2011

Source: World Health Organization, United Nations Children’s Fund, and World Development Indicators database.

Five million deaths averted in 20 countries

4e

Deaths of children under age 5, 2011 (millions) At 2011 mortality rate

Averted based on 1990 mortality rate

India Nigeria China Pakistan Ethiopia Bangladesh Indonesia Tanzania Afghanistan Uganda Niger Mozambique Angola Brazil Egypt, Arab Rep. Malawi Philippines Madagascar Mexico Turkey 0

1

2

3

4

Source: World Bank staff calculations.

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World Development Indicators 2013 9


Goal 5 Improve maternal health Maternal deaths are more likely in South Asia and Sub-­Saharan Africa

5a

Maternal mortality ratio, modeled estimate (per 100,000 live births) 1,000 Sub-Saharan Africa 750 South Asia 500

250

Latin America & Caribbean

Middle East & North Africa East Asia & Pacific

0

Europe & Central Asia 1990

1995

2000

2005

2010

Source: Maternal Mortality Estimation Inter-Agency Group and World Development Indicators database.

Progress toward reducing maternal mortality, 1990–2010

5b

Share of countries making progress toward reducing maternal mortality (%) 100

50

0

50

100

Reached target Insufficient data

On track

Off track

Seriously off track

East Asia & Pacific

Europe Latin Middle East & Central America & & North Asia Caribbean Africa Source: World Bank staff calculations.

South Asia

Sub-Saharan Africa

Reducing the risk to mothers

5c

Lifetime risk of maternal death (%) 6

4

2010

0

1990

2

Europe Latin Middle East South Sub-Saharan & Central America & & North Asia Africa Asia Caribbean Africa Source: Maternal Mortality Estimation Inter-Agency Group and World Development Indicators database.

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An estimated 287,000 maternal deaths occurred worldwide in 2010, all but 1,700 of them in developing countries. More than half of maternal deaths occur in Sub-­S aharan Africa and a quarter in South Asia. And while the number of maternal deaths remains high, South Asia has made great progress in reducing them, reaching a maternal mortality ratio of 220 per 100,000 live births in 2010, down from 620 in 1990, a drop of 65 percent. The Middle East and North Africa and East Asia and Pacific have also reduced their maternal morality ratios more than 60 percent (figure 5a). These are impressive achievements, but progress in reducing maternal mortality has been slow, far slower than targeted by the Millennium Development Goals, which call for reducing the maternal mortality ratio by 75 percent between 1990 and 2015. But few countries and no developing region on average will achieve this target. Based on progress through 2010, 8 countries have achieved a 75 percent reduction, and 10 more are on track to reach the 2015 target (figure 5b). This is an improvement over the 2008 assessment, suggesting that progress is accelerating. Because of the reductions in Cambodia, China, Lao People’s Democratic Republic, and Vietnam, 74 percent of people in East Asia and Pacific live in a country that has reached the target (Vietnam) or is on track to do so by 2015. On average a third of people in low- and middleincome countries live in countries that have reached the target or are on track to do so. The maternal mortality ratio gives the risk of a maternal death at each birth, a risk compounded with each pregnancy. And because women in poor countries have more children under riskier conditions, their lifetime risk of maternal death may be 100 times greater than for women in highincome countries. Improved health care and lower fertility rates have reduced the lifetime risk in all regions, but women ages 15–49 in Sub-­Saharan Africa still face a 2.5 percent chance of dying

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in childbirth, down from more than 5 percent in 1990 (figure 5c). In Chad and Somalia, both fragile states, lifetime risk is still more than 6 percent, meaning more than 1 woman in 16 will die in childbirth. Reducing maternal mortality requires a comprehensive approach to women’s reproductive health, starting with family planning and access to contraception. In countries with data half of women who are married or in union use some method of birth control. Household surveys of women show that some 200 million women want to delay or cease childbearing, and a substantial proportion say that their last birth was unwanted or mistimed (United Nations 2012). Figure 5d shows the share of women of childbearing age who say they need but are not using contraception. There are large differences within each region. More surveys have been carried out in Sub-­Saharan Africa than in any other region, and many show a large unmet need for family planning. Women who give birth at an early age are likely to bear more children and are at greater risk of death or serious complications from pregnancy. The adolescent birth rate is highest in Sub-­ Saharan Africa and is declining slowly. A rapid decrease in South Asia has been led by Maldives, Afghanistan, and Pakistan (figure 5e). Many health problems among pregnant women are preventable or treatable through visits with trained health workers before childbirth. Skilled attendants at delivery and access to hospital treatments are essential for dealing with lifethreatening emergencies such as severe bleeding and hypertensive disorders. In South Asia and Sub-­Saharan Africa fewer than half of births are attended by doctors, nurses, or trained midwives (figure 5f). Having skilled health workers present for deliveries is key to reducing maternal mortality. In many places women have only untrained caregivers or family members to attend them during childbirth.

Economy

States and markets

A wide range of needs

5d

Unmet need for contraception, most recent year available, 2006–10 (% of women married or in union ages 15–49) 50

Regional median

40

30 20

10 0

East Asia & Pacific

(7 countries)

Europe & Central Asia

(10 countries)

Latin America & Caribbean

(9 countries)

Middle East & North Africa

South Asia

(5 countries)

Sub-Saharan Africa (31 countries)

(6 countries)

Source: Demographic and Household Surveys, Multiple Indicator Cluster Surveys, and World Development Indicators database.

Fewer young women giving birth

5e

Adolescent fertility rate (births per 1,000 women ages 15–19) 150 Sub-Saharan Africa South Asia 100 Latin America & Caribbean 50

Middle East & North Africa Europe & Central Asia East Asia & Pacific

0

1997

1999

2001

2003

2005

2007

2009

2011

Source: United Nations Population Division and World Development Indicators database.

Every mother needs care

5f

Births attended by skilled health staff, average of most recent year available for 2007–11 (% of total) 100

75

50

25

0

East Asia & Pacific

Europe Latin Middle East South Sub-Saharan & Central America & & North Asia Africa Asia Caribbeana Africaa a. Data are for 1998–2002. Source: United Nations Children’s Fund and World Development Indicators database.

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World Development Indicators 2013 11


Goal 6 Combat HIV/AIDS, malaria, and other diseases HIV prevalence in Sub-­Saharan Africa continues to fall

6a

HIV prevalence (% of population ages 15–49) 6

Sub-Saharan Africa

5 4 3 2

Latin America & Caribbean High income

1 0

Other developing regions

1990

1995

2000

2005

2011

Source: Joint United Nations Programme on HIV/AIDS and World Development Indicators database.

Progress toward halting and reversing the HIV epidemic, 1990–2010

6b

Share of countries making progress against HIV/AIDS (%) 100

50

0

50 Halted and reversed Not improving

100

Halted or reversed Insufficient data

Stable low prevalence

East Asia & Pacific

Europe Latin Middle East & Central America & & North Asia Caribbean Africa Source: World Bank staff calculations.

South Asia

Sub-Saharan Africa

Knowledge to control HIV/AIDS

6c

People with comprehensive and correct knowledge about HIV, most recent year available (% of adults ages 15–49) 75

50

nia za Ta n

da

Ke ny a

Ug an

Mo z

Ma law i

mb ia am biq ue

bia

Za

mi Na

o th

ba Zim

d ila n az Sw

Le so

0

bw e

Women Men

25

Epidemic diseases exact a huge toll in human suffering and lost opportunities for development. Poverty, armed conflict, and natural disasters contribute to the spread of disease and are made worse by it. In Africa the spread of HIV/AIDS has reversed decades of improvement in life expectancy and left millions of children orphaned. Malaria takes a large toll on young children and weakens adults at great cost to their productivity. Tuberculosis killed 1.4 million people in 2010, most of them ages 15–45, and sickened millions more. There were 34 million people living with HIV/ AIDS in 2011, and 2.5 million more people acquired the disease. Sub-­Saharan Africa remains the center of the HIV/AIDS epidemic, but the proportion of adults living with AIDS has begun to fall even as the survival rate of those with access to antiretroviral drugs has increased (figures 6a and 6b). By the end of 2010, 6.5 million people worldwide were receiving antiretroviral drugs. This represented the largest one-year increase in coverage but still fell far short of universal access (United Nations 2012). Altering the course of the HIV epidemic requires changes in behaviors by those already infected by the virus and those at risk of becoming infected. Knowledge of the cause of the disease, its transmission, and what can be done to avoid it is the starting point. The ability to reject false information is another important kind of knowledge. But significant gaps in knowledge remain. In 26 of 31 countries with a generalized epidemic and in which nationally representative surveys were carried out recently, less than half of young women have comprehensive and correct knowledge about HIV (UNAIDS 2012). And less than half of men in 21 of 25 countries had correct knowledge. In only

Note: Comprehensive and correct knowledge about HIV entails knowledge of two ways to prevent HIV and rejecting three misconceptions. Source: Joint United Nations Programme on HIV/AIDS and World Development Indicators database.

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3 of the 10 countries with the highest HIV prevalence rates in 2011 did more than half the men and women tested demonstrate knowledge of two ways to prevent HIV and reject three misconceptions (figure 6c). In Kenya men scored better than 50  percent, but women fell short. Clearly more work is to be done. In 2011 there were 8.8  million people newly diagnosed with tuberculosis, but incidence, prevalence, and death rates from tuberculosis are falling (figure 6d). If these trends are sustained, the world could achieve the target of halting and reversing the spread of this disease by 2015. People living with HIV/AIDS, which reduces resistance to tuberculosis, are particularly vulnerable, as are refugees, displaced persons, and prisoners living in close quarters and unsanitary conditions. Well-managed medical intervention using appropriate drug therapy is crucial to stopping the spread of tuberculosis. There are 300–500 million cases of malaria each year, causing more than 1 million deaths. Malaria is a disease of poverty. But there has been progress against it. In 2011 Armenia was added to the list of countries certified free of the disease. Although malaria occurs in all regions, Sub-­Saharan Africa is where the most lethal form of the malaria parasite is most abundant. Insecticide-treated nets have proved to be an effective preventative, and their use in the region is growing: from 2 percent of the population under age 5 in 2000 to 39 percent in 2010 (figure 6e). Better testing and the use of combination therapies with artemisinin-based drugs are improving the treatment of at-risk populations. But malaria is difficult to control. There is evidence of emerging resistance to artemisinins and to pyrethroid insecticides used to treat mosquito nets.

Fewer people contracting, living with, and dying from tuberculosis

6d

Tuberculosis prevalence, incidence, and deaths in low- and middle-income countries (per 100,000 people) 400 Prevalence 300

200 Incidence 100 Deaths 0

1990

1995

2000

2005

2011

Source: World Health Organization and World Development Indicators database.

Use of insecticide-treated nets increasing in Sub- ­Saharan Africa

6e

Use of insecticide-treated nets (% of population under age 5) First observation (2000 or earlier)

Most recent observation (2006 or later)

Swaziland Côte d’Ivoire Guinea Comoros Zimbabwe Chad Somalia Mozambique Benin Cameroon Sudan Angola Nigeria Sierra Leone Ethiopia Gambia, The Namibia Senegal Guinea-Bissau Central African Rep. Liberia Congo, Dem. Rep. Ghana Malawi Uganda Burundi Madagascar Kenya Burkina Faso Eritrea Zambia Gabon São Tomé and Príncipe Togo Tanzania Niger Rwanda Mali Congo, Rep. (2005) Mauritania (2004) 0

20

40

60

80

Source: United Nations Children’s Fund and World Development Indicators database.

Economy

States and markets

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World Development Indicators 2013 13


Goal 7 Ensure environmental sustainability Carbon dioxide emissions dropped slightly in 2009

7a

Carbon dioxide emissions (millions of metric tons) 40

30

20

High income

10 Upper middle income 0

Low income

Lower middle income 1990

1995

2000

2005

2009

Source: Carbon Dioxide Information Analysis Center and World Development Indicators database.

Forest losses and gains

7b

Average annual change in forest area (thousands of square kilometers)

1990–2000

0

–250

–500

2000–10

250

East Asia & Pacific

Europe Latin Middle East South Sub-Saharan High & Central America & & North Asia Africa income Asia Caribbean Africa Source: Food and Agriculture Organization and World Development Indicators database.

Better access to improved water sources

7c

Share of population with access to improved water sources (%) 100

75

50

2010

0

1990

25

Europe Latin Middle East South Sub-Saharan & Central America & & North Asia Africa Asia Caribbean Africa Source: Joint Monitoring Programme of the World Health Organization and United Nations Children’s Fund and World Development Indicators database.

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The seventh goal of the Millennium Development Goals is the most far-reaching, affecting each person now and in the future. It addresses the condition of the natural and built environments: reversing the loss of natural resources, preserving biodiversity, increasing access to safe water and sanitation, and improving living conditions of people in slums. The overall theme is sustainability, an equilibrium in which people’s lives can improve without depleting natural and manmade capital stocks. The failure to reach a comprehensive agreement on limiting greenhouse gas emissions leaves billions of people vulnerable to climate change. Although the global financial crisis caused a slight decrease in carbon dioxide emissions, such emissions are expected to rise as economic activity resumes in large industrial economies (figure 7a). The loss of forests threatens the livelihood of poor people, destroys the habitat that harbors biodiversity, and eliminates an important carbon sink that helps moderate the climate. Net losses since 1990 have been substantial, especially in Latin American and the Caribbean and Sub-­Saharan Africa, and only partly compensated by net gains elsewhere. The rate of deforestation slowed in the past decade, but on current trends zero net losses will not be reached for another 20 years (figure 7b). Protecting forests and other terrestrial and marine areas helps protect plant and animal habitats and preserve the diversity of species. By 2010, 13  percent of the world’s land area had been protected, but only 1.6 percent of oceans had similar protection. Such measures have slowed the rate of species extinction, but substantial losses continue (United Nations 2012). The Millennium Development Goals call for halving the proportion of the population without access to improved sanitation facilities and water sources by 2015. In 1990 more than 1 billion people lacked access to drinking water from a convenient, protected source. In developing countries the proportion of people with access to an

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Economy

States and markets

Rural areas lack sanitation facilities

7d

Share of population with access to improved sanitation facilities, 2010 (%) 100

75

50

0

Urban

25 Rural

improved water source rose from 71 percent in 1990 to 86 in 2010 (figure 7c). In 1990 only 37 percent of the people living in low- and middle-income economies had access to a flush toilet or other form of improved sanitation. By 2010 the access rate had risen to 56 percent. But 2.7 billion people still lack access to improved sanitation, and more than 1 billion practice open defecation, posing enormous health risks. The situation is worse in rural areas, where 43 percent of the population have access to improved sanitation; in urban areas the access rate is 30 percentage points higher (figure 7d). This large disparity, especially in South Asia and Sub-­Saharan Africa, is the principal reason the sanitation target of the Millennium Development Goals will not be met. Achieving sustainable development also requires managing natural resources carefully, since high economic growth can deplete natural capital, such as forests and minerals. Countries that rely heavily on extractive industries have seen large increases in natural resource rents, but their growth will not be sustainable unless they invest in productive assets, including human capital (figure 7e). The World Bank has constructed a global database to monitor the sustainability of economic progress using wealth accounts, including indicators of adjusted net savings and adjusted net national income. Wealth is defined comprehensively to include stocks of manufactured capital, natural capital, human capital, and social capital. Development is conceived as building wealth and managing a portfolio of assets. The challenge of development is to manage not just the total volume of assets but also the composition of the asset portfolio—that is, how much to invest in different types of capital. Adjusted net savings is a sustainability indicator that measures whether a country is building its wealth sustainably (positive values) or running it down on an unsustainable development path (negative values; figure 7f).

East Asia & Pacific

Europe Latin Middle East South Sub-Saharan & Central America & & North Asia Africa Asia Caribbean Africa Source: Joint Monitoring Programme of the World Health Organization and United Nations Children’s Fund and World Development Indicators database.

Resource rents are a large share of GDP in Africa and the Middle East

7e

Resource rents (% of GDP) 50

40

Middle East & North Africa

30

Europe & Central Asia

20 Sub-Saharan Africa 10

0

East Asia & Pacific South Asia

Latin America & Caribbean 1990

1995

2000

2005

2010

Source: World Bank staff calculations and World Development Indicators database.

A nonsustainable path in Sub‑Saharan Africa

7f

Share of gross national income, 2008 (%) 20 Depreciation of fixed capital

Education expenditures

10

Depletion of natural resources

0

Pollution damages

–10

Gross savings

Net savings

Net savings plus education expeditures

Depletion adjusted savings

Adjusted net savings

Source: World Bank 2011.

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World Development Indicators 2013 15


Goal 8 Develop a global partnership for development Aid flows decline

8a

Official development assistance from Development Assistance Committee members (2010 $ billions) 200 Humanitarian assistance

150 Net debt relief

100

Bilateral net official development assistance, excluding debt relief and humanitarian assistance

50

0

Multilateral net official development assistance, excluding debt relief and humanitarian assistance

1990

1995

2000

2005

2011

Source: Organisation for Economic Co-operation and Development StatExtracts.

Domestic subsidies to agriculture exceed aid flows

8b

Agricultural support ($ billions) 150

European Union 100 Japan 50 United States Korea, Rep. 0

Turkey 1990

1995

2000

2005

2011

Source: Organisation for Economic Co-operation and Development StatExtracts.

More opportunities for exporters in developing countries

8c

Goods (excluding arms) admitted free of tariffs from developing countries (% total merchandise imports, excluding arms) 100 United States

Norway 75

Australia Japan

50 European Union 25

0

1996

1998

2000

2002

2004

2006

2008

2010

Source: World Trade Organization, International Trade Center, United Nations Conference on Trade and Development, and World Development Indicators database.

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The eighth and final goal distinguishes the Millennium Development Goals from previous resolutions and targeted programs. It recognizes the multidimensional nature of development and the need for wealthy countries and developing countries to work together to create an environment in which rapid, sustainable development is possible. Along with increased aid flows and debt relief for the poorest, highly indebted countries, goal 8 recognizes the need to reduce barriers to trade and to share the benefits of new medical and communication technologies. It is also a reminder that development challenges differ for large and small countries and for those that are landlocked or isolated by large expanses of ocean. Building and sustaining a partnership is an ongoing process that does not stop at a specific date or when a target is reached. After falling through much of the 1990s, official development assistance (ODA) from members of the Organisation for Economic Co-operation and Development’s (OECD) Development Assistance Committee (DAC) rose sharply after 2002, but a large part of the increase was in the form of debt relief and humanitarian assistance (figure 8a). The financial crisis that began in 2008 and fiscal austerity in many high-income economies have begun to undermine commitments to increase ODA. Net disbursements of ODA by members of the DAC rose to $134 billion in 2011, but, after accounting for price and exchange rate adjustments, fell 2.3 percent in real terms from 2010. Aid from multilateral organizations remained essentially unchanged at $34.7 billion, a decrease of 6.6 percent in real terms. ODA from DAC members has fallen back to 0.31 percent of their combined gross national income, less than half the UN target of 0.7 percent. OECD members, mostly high-income economies but also some upper middle-income economies such as Chile, Mexico, and Turkey, continue to spend more on support to domestic agricultural producers than on ODA. In 2011 the OECD

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estimate of agricultural subsidies was $252 billion, 41  percent of which was to EU producers (figure 8b). Many rich countries have pledged to open their markets to exports from developing countries, and the share of goods (excluding arms) admitted duty free by OECD economies has been rising. However, arcane rules of origin and phytosanitary standards keep many countries from qualifying for duty-free access. And uncertainty over market access may inhibit development of export industries (figure 8c). Growing economies, better debt management, and debt relief for the poorest countries have allowed developing countries to substantially reduce their debt burdens. Despite the financial crisis and a 2.3 percent contraction in the global economy in 2009, the debt service to exports ratio in low- and middle-income economies reached a new low of 8.8 percent in 2011. In Europe and Central Asia, where the debt service to exports ratio rose to 26 percent in 2009, higher export earnings have helped return the average to its 2007 level of 17.8 percent (figure 8d). Telecommunications is an essential tool for development, and new technologies are creating new opportunities everywhere. The growth of fixedline phone systems has peaked in high-income economies and will never reach the same level of use in developing countries. In high-income economies mobile phone subscriptions have now passed 1 per person, and upper middle-income economies are not far behind (figure 8e). Mobile phones are one of several ways of accessing the Internet. In 2000 Internet use was spreading rapidly in high-income economies but was barely under way in developing country regions. Now developing countries are beginning to catch up. Since 2000 Internet use per person in developing economies has grown 28 percent a year. Like telephones, Internet use is strongly correlated with income. The low-income economies of South Asia and Sub-­Saharan Africa lag behind, but even there Internet access is spreading rapidly (figure 8f).

Economy

States and markets

Debt service burdens continue to fall

8d

Total debt service (% of exports of goods, services, and income) 50 Latin America & Caribbean

40 South Asia

Europe & Central Asia

30

20 East Asia & Pacific

10

Sub-Saharan Africa 0

Middle East & North Africa 1990

1995

2000

2005

2011

Source: World Development Indicators database.

Telecommunications on the move

8e

Mobile phone subscriptions (per 100 people) 125 High income 100 Upper middle income

75

50

Lower middle income

25 Low income 0

1990

1995

2000

2005

2011

Source: International Telecommunications Union and World Development Indicators database.

More people connecting to the Internet

8f

Internet users (per 100 people) 80 High income

60

Europe & Central Asia 40 Latin America & Caribbean East Asia & Pacific 20

0

Sub-Saharan Africa

Middle East & North Africa

South Asia 2000

2002

2004

2006

2008

2011

Source: International Telecommunications Union and World Development Indicators database.

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World Development Indicators 2013 17


Millennium Development Goals

Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 1 Eradicate extreme poverty and hunger Target 1.A Halve, between 1990 and 2015, the proportion of people whose income is less than $1 a day

Target 1.B Achieve full and productive employment and decent work for all, including women and young people

Target 1.C Halve, between 1990 and 2015, the proportion of people who suffer from hunger Goal 2 Achieve universal primary education Target 2.A Ensure that by 2015 children everywhere, boys and girls alike, will be able to complete a full course of primary schooling Goal 3 Promote gender equality and empower women Target 3.A Eliminate gender disparity in primary and secondary education, preferably by 2005, and in all levels of education no later than 2015

Goal 4 Reduce child mortality Target 4.A Reduce by two-thirds, between 1990 and 2015, the under-five mortality rate

Goal 5 Improve maternal health Target 5.A Reduce by three-quarters, between 1990 and 2015, the maternal mortality ratio Target 5.B Achieve by 2015 universal access to reproductive health

Goal 6 Combat HIV/AIDS, malaria, and other diseases Target 6.A Have halted by 2015 and begun to reverse the spread of HIV/AIDS

Target 6.B Achieve by 2010 universal access to treatment for HIV/AIDS for all those who need it Target 6.C Have halted by 2015 and begun to reverse the incidence of malaria and other major diseases

1.1 Proportion of population below $1 purchasing power parity (PPP) a daya 1.2 Poverty gap ratio [incidence × depth of poverty] 1.3 Share of poorest quintile in national consumption 1.4 Growth rate of GDP per person employed 1.5 Employment to population ratio 1.6 Proportion of employed people living below $1 (PPP) a day 1.7 Proportion of own-account and contributing family workers in total employment 1.8 Prevalence of underweight children under five years of age 1.9 Proportion of population below minimum level of dietary energy consumption 2.1 Net enrollment ratio in primary education 2.2 Proportion of pupils starting grade 1 who reach last grade of primary education 2.3 Literacy rate of 15- to 24-year-olds, women and men 3.1 Ratios of girls to boys in primary, secondary, and tertiary education 3.2 Share of women in wage employment in the nonagricultural sector 3.3 Proportion of seats held by women in national parliament 4.1 Under-five mortality rate 4.2 Infant mortality rate 4.3 Proportion of one-year-old children immunized against measles 5.1 5.2 5.3 5.4 5.5

Maternal mortality ratio Proportion of births attended by skilled health personnel Contraceptive prevalence rate Adolescent birth rate Antenatal care coverage (at least one visit and at least four visits) 5.6 Unmet need for family planning 6.1 HIV prevalence among population ages 15–24 years 6.2 Condom use at last high-risk sex 6.3 Proportion of population ages 15–24 years with comprehensive, correct knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of nonorphans ages 10–14 years 6.5 Proportion of population with advanced HIV infection with access to antiretroviral drugs 6.6 Incidence and death rates associated with malaria 6.7 Proportion of children under age five sleeping under insecticide-treated bednets 6.8 Proportion of children under age five with fever who are treated with appropriate antimalarial drugs 6.9 Incidence, prevalence, and death rates associated with tuberculosis 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course

Note: The Millennium Development Goals and targets come from the Millennium Declaration, signed by 189 countries, including 147 heads of state and government, in September 2000 (www.un.org/millennium/declaration/ares552e.htm) as updated by the 60th UN General Assembly in September 2005. The revised Millennium Development Goal (MDG) monitoring framework shown here, including new targets and indicators, was presented to the 62nd General Assembly, with new numbering as recommended by the Inter-agency and Expert Group on MDG Indicators at its 12th meeting on November 14, 2007. The goals and targets are interrelated and should be seen as a whole. They represent a partnership between the developed countries and the developing countries “to create an environment—at the national and global levels alike—which is conducive to development and the elimination of poverty.” All indicators should be disaggregated by sex and urban-rural location as far as possible.

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Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 7 Ensure environmental sustainability Target 7.A Integrate the principles of sustainable development into country policies and programs and reverse the loss of environmental resources Target 7.B Reduce biodiversity loss, achieving, by 2010, a significant reduction in the rate of loss

Target 7.C Halve by 2015 the proportion of people without sustainable access to safe drinking water and basic sanitation Target 7.D Achieve by 2020 a significant improvement in the lives of at least 100 million slum dwellers Goal 8 Develop a global partnership for development Target 8.A Develop further an open, rule-based, predictable, nondiscriminatory trading and financial system

7.1 Proportion of land area covered by forest 7.2 Carbon dioxide emissions, total, per capita and per $1 GDP (PPP) 7.3 Consumption of ozone-depleting substances 7.4 Proportion of fish stocks within safe biological limits 7.5 Proportion of total water resources used 7.6 Proportion of terrestrial and marine areas protected 7.7 Proportion of species threatened with extinction 7.8 Proportion of population using an improved drinking water source 7.9 Proportion of population using an improved sanitation facility 7.10 Proportion of urban population living in slumsb

Some of the indicators listed below are monitored separately for the least developed countries (LDCs), Africa, landlocked developing countries, and small island developing states.

(Includes a commitment to good governance, development, and poverty reduction—both nationally and internationally.)

Target

Target

Target

Target

Target

Official development assistance (ODA) 8.1 Net ODA, total and to the least developed countries, as percentage of OECD/DAC donors’ gross national income 8.2 Proportion of total bilateral, sector-allocable ODA of OECD/DAC donors to basic social services (basic education, primary health care, nutrition, safe water, and 8.B Address the special needs of the least developed sanitation) countries 8.3 Proportion of bilateral official development assistance of OECD/DAC donors that is untied (Includes tariff and quota-free access for the least developed countries’ exports; enhanced program of 8.4 ODA received in landlocked developing countries as a proportion of their gross national incomes debt relief for heavily indebted poor countries (HIPC) 8.5 ODA received in small island developing states as a and cancellation of official bilateral debt; and more proportion of their gross national incomes generous ODA for countries committed to poverty reduction.) Market access 8.C Address the special needs of landlocked 8.6 Proportion of total developed country imports (by value developing countries and small island developing and excluding arms) from developing countries and least states (through the Programme of Action for developed countries, admitted free of duty the Sustainable Development of Small Island 8.7 Average tariffs imposed by developed countries on Developing States and the outcome of the 22nd agricultural products and textiles and clothing from special session of the General Assembly) developing countries 8.8 Agricultural support estimate for OECD countries as a percentage of their GDP 8.9 Proportion of ODA provided to help build trade capacity 8.D Deal comprehensively with the debt problems of developing countries through national and Debt sustainability international measures in order to make debt 8.10 Total number of countries that have reached their HIPC sustainable in the long term decision points and number that have reached their HIPC completion points (cumulative) 8.11 Debt relief committed under HIPC Initiative and Multilateral Debt Relief Initiative (MDRI) 8.12 Debt service as a percentage of exports of goods and services 8.E In cooperation with pharmaceutical companies, 8.13 Proportion of population with access to affordable provide access to affordable essential drugs in essential drugs on a sustainable basis developing countries 8.F In cooperation with the private sector, make 8.14 Fixed-line telephones per 100 population available the benefits of new technologies, 8.15 Mobile cellular subscribers per 100 population especially information and communications 8.16 Internet users per 100 population

a. Where available, indicators based on national poverty lines should be used for monitoring country poverty trends. b. T he proportion of people living in slums is measured by a proxy, represented by the urban population living in households with at least one of these characteristics: lack of access to improved water supply, lack of access to improved sanitation, overcrowding (three or more people per room), and dwellings made of nondurable material.

Economy

States and markets

Global links

Back

World Development Indicators 2013 19


1  World view Population

Population density

Urban population

Gross national income

millions

thousand sq. km

people per sq. km

% of total population

$ billions

Per capita $

$ billions

Per capita $

% growth

Per capita % growth

2011

2011

2011

2011

2011

2011

Atlas method

Purchasing power parity

2011

2011

35.3

652.2

54

24

16.6

470

Albania

3.2

28.8

117

53

12.8

Algeria

36.0

2,381.7

15

73

160.8

American Samoa

0.1

0.2

348

93

..

..b

..

Andorra

0.1

0.5

183

87

..

..c

..

19.6

1,246.7

16

59

75.2

3,830 d

102.7

Afghanistan

Angola Antigua and Barbuda

Gross domestic product

2010–11

2010–11

40.3a

1,140a

5.7

2.9

3,980

28.4

8,820

3.0

2.6

4,470

299.0a

8,310a

2.5

1.0

..

..

..

..

..

..

5,230

3.9

1.1

17,900a

–5.0

–6.0

1.6a

0.1

0.4

204

30

1.1

11,940

40.8

2,780.4

15

93

..

..

..

..

..

..

Armenia

3.1

29.7

109

64

10.4

3,360

18.9

6,100

4.6

4.3

Aruba

0.1

0.2

601

47

..

..

..

..

..

22.3

7,741.2

3

89

1,111.4

49,790

862.0

38,610

1.9

0.7

Austria

8.4

83.9

102

68

405.7

48,170

354.0

42,030

2.7

2.3

Azerbaijan

9.2

86.6

111

54

48.5

5,290

82.1

8,950

1.0

–0.3

Bahamas, The

0.3

13.9

35

84

7.5

21,970

10.2a

29,790a

1.6

0.4

Bahrain

1.3

0.8

1,742

89

20.1

15,920

26.8

21,200

3.0

–3.1

150.5

144.0

1,156

28

117.8

780

291.7

0.3

0.4

637

44

3.5

12,660

Belarus

9.5

207.6

47

75

55.2

5,830

137.0

Belgium

11.0

30.5

364

98

506.2

45,930

431.4

0.4

23.0

16

45

1.3

3,710

Benin

9.1

112.6

82

45

7.1

780

Bermuda

0.1

0.1

1,294

100

..

Argentina

Australia

Bangladesh Barbados

Belize

..c

..c

5.2a

2.2a

1,940

6.7

5.4

18,900a

1.2

–5.5

14,460

5.3

5.5

39,150

1.8

0.6

1.9

–1.5

6,090a

14.6

1,610

3.5

0.7

..

..

–1.9

–1.7

Bhutan

0.7

38.4

19

36

1.6

2,130

4.1

5,570

5.6

3.8

Bolivia

10.1

1,098.6

9

67

20.4

2,020

49.3

4,890

5.2

3.5

Bosnia and Herzegovina

3.8

51.2

74

48

18.0

4,780

34.5

9,190

1.7

1.9

Botswana

2.0

581.7

4

62

15.2

7,470

29.5

14,550

5.7

4.5

196.7

8,514.9

23

85

2,107.7

10,720

2,245.8

11,420

2.7

1.8

Brazil Brunei Darussalam

0.4

5.8

77

76

12.5

31,800

19.6

49,910

2.2

0.4

Bulgaria

7.3

111.0

68

73

48.8

6,640

105.8

14,400

1.7

4.3

17.0

274.2

62

27

9.9

580

22.5

1,330

4.2

1.1

8.6

27.8

334

11

2.2

250

5.2

610

4.2

1.9

Cambodia

14.3

181.0

81

20

11.7

820

31.8

2,230

7.1

5.8

Cameroon

20.0

475.4

42

52

24.1

1,210

46.7

2,330

4.2

2.0

Canada

34.5

9,984.7

4

81

1,570.9

45,550

1,367.6

39,660

2.5

1.4

3,540

2.0

3,980

5.0

4.1

..

..

..

..

Burkina Faso Burundi

Cape Verde

0.5

4.0

124

63

1.8

Cayman Islands

0.1

0.3

236

100

..

Central African Republic

4.5

623.0

7

39

2.1

480

3.6

810

3.3

1.3

11.5

1,284.0

9

22

8.3

720

17.7

1,540

1.6

–1.0

0.2

0.2

810

31

..

..

..

..

..

17.3

756.1

23

89

212.0

12,280

282.1

16,330

6.0

5.0 8.8

Chad Channel Islands Chile China

..c

..c

1,344.1

9,600.0

144

51

6,643.2

4,940

11,270.8

8,390

9.3

Hong Kong SAR, China

7.1

1.1

6,787

100

254.6

36,010

370.2

52,350

4.9

4.8

Macao SAR, China

0.6

0.0e

19,848

100

24.7

45,460

31.0

56,950

20.7

18.1

Colombia

46.9

1,141.8

42

75

284.9

6,070

448.6

9,560

5.9

4.5

Comoros

0.8

1.9

405

28

0.6

770

0.8

1,110

2.2

–0.4

67.8

2,344.9

30

34

13.1

190

23.2

340

6.9

4.1

4.1

342.0

12

64

9.3

2,250

13.4

3,240

3.4

1.0

Congo, Dem. Rep. Congo, Rep.

20 

Surface area

World Development Indicators 2013

Front

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User guide

World view People Environment


World view  1 Population

Costa Rica Côte d’Ivoire Croatia

Surface area

Population density

Urban population

Gross national income

millions

thousand sq. km

people per sq. km

% of total population

$ billions

Per capita $

$ billions

2011

2011

2011

2011

2011

2011

2011

Atlas method

Purchasing power parity

Gross domestic product

Per capita $

% growth

Per capita % growth 2010–11

2011

2010–11

4.7

51.1

93

65

36.1

7,640

56.1a

11,860a

4.2

2.7

20.2

322.5

63

51

22.1

1,090

34.5

1,710

–4.7

–6.7

13,540

4.4

56.6

79

58

59.6

82.7

18,780

0.0

0.3

11.3

109.9

106

75

..

..b

..

..

2.1

2.1

Curaçao

0.1

0.4

328

..

..

..c

..

..

..

..

Cyprus

1.1

9.3

121

71

23.7f

29,450 f

24.9 f

30,970 f

0.5f

0.3f

Cuba

Czech Republic

10.5

78.9

136

73

196.3

18,700

257.0

24,490

1.9

2.1

Denmark

5.6

43.1

131

87

335.1

60,160

233.5

41,920

1.1

0.7

Djibouti

0.9

23.2

39

77

1.1

1,270

2.1

2,450

5.0

3.0

Dominica

0.1

0.8

90

67

0.5

7,030

0.9a

13,000a

–0.3

–0.2

Dominican Republic

10.1

48.7

208

70

52.6

5,240

94.7a

9,420a

4.5

3.1

Ecuador

14.7

256.4

59

67

61.7

4,200

124.7

8,510

7.8

6.3

Egypt, Arab Rep.

82.5

1,001.5

83

44

214.7

2,600

504.8

6,120

1.8

0.1

6.2

21.0

301

65

21.7

3,480

41.4 a

6,640a

1.5

0.9

18.4

7.8

4.8

El Salvador Equatorial Guinea

0.7

28.1

26

40

11.3

15,670

Eritrea

5.4

117.6

54

21

2.3

430

Estonia

1.3

45.2

32

70

20.4

15,260

27.9

Ethiopia

84.7

1,104.3

85

17

31.0

370

1.4

35

41

..

48

52

3.2

25,620 580a

8.7

5.5

20,850

8.3

8.3

93.8

1,110

7.3

5.0

..

..

..

..

4.0

4,610

2.0

1.1

Faeroe Islands

0.0 g

Fiji

0.9

18.3

Finland

5.4

338.4

18

84

257.3

47,760

202.9

37,660

2.7

2.3

France

65.4

549.2

120

86

2,775.7

42,420

2,349.8

35,910

1.7

1.1

French Polynesia

0.3

4.0

75

51

..

..

..

..

..

Gabon

1.5

267.7

6

86

12.4

21.1

13,740

4.8

2.8

Gambia, The

1.8

11.3

178

–4.3

–6.9

Georgia

4.5h

69.7

79 h

..c

3.1a

3,720

..c 8,080

57

0.9

500

3.1

1,750

53h

12.8h

2,860h

24.0h

5,350h

7.0h

6.2h

Germany

81.8

357.1

235

74

3,617.7

44,230

3,287.6

40,190

3.0

3.0

Ghana

25.0

238.5

110

52

35.1

1,410

45.2

1,810

14.4

11.8

Greece

11.3

132.0

88

62

276.7

24,490

283.7

25,110

–7.1

–7.0

0.1

410.5i

0j

85

1.5

26,020

..

..

–5.4

–5.4

Grenada

0.1

0.3

309

39

0.8

7,350

1.1a

1.0

0.6

Guam

0.2

0.5

337

93

..

Guatemala

14.8

108.9

138

50

42.4

2,870

Guinea

Greenland

..c

.. 70.3a

10,350a ..

..

..

4,760a

3.9

1.3

10.2

245.9

42

36

4.4

430

10.5

1,020

3.9

1.5

Guinea-Bissau

1.5

36.1

55

44

0.9

600

1.9

1,230

5.7

3.5

Guyana

0.8

215.0

4

28

2.2

2,900

2.6 a

3,460a

4.2

4.2

10.1

27.8

367

53

7.1

700

11.9a

1,180a

5.6

4.2

7.8

112.5

69

52

15.4

1,980

29.7a

3,820a

3.6

1.6

Hungary

10.0

93.0

110

69

126.9

12,730

202.5

20,310

1.7

2.0

Iceland

0.3

103.0

3

94

11.1

34,820

9.9

31,020

2.6

2.2

Haiti Honduras

India

1,241.5

3,287.3

418

31

1,766.2

1,420

4,524.6

3,640

6.3

4.9

242.3

1,904.6

134

51

712.7

2,940

1,091.4

4,500

6.5

5.4

Iran, Islamic Rep.

74.8

1,745.2

46

69

330.4

4,520

835.5

11,420

..

..

Iraq

33.0

435.2

76

67

87.0

2,640

123.5

3,750

9.9

6.8

Ireland

4.6

70.3

66

62

179.2

39,150

153.4

33,520

0.7

–1.5

Isle of Man

0.1

0.6

146

51

..

..

..

..

..

Israel

7.8

22.1

359

92

224.7

210.5

27,110

4.7

2.8

Indonesia

Economy

States and markets

Global links

..c 28,930

Back

World Development Indicators 2013 21


1  World view Population

Italy Jamaica Japan Jordan

Population density

Urban population

millions

thousand sq. km

people per sq. km

% of total population

2011

Gross national income Atlas method

$ billions

Purchasing power parity

Per capita $

$ billions

Gross domestic product

Per capita $

% growth

Per capita % growth

2011

2011

2011

2011

2011

2011

2011

2010–11

2010–11

60.7

301.3

206

68

2,144.7

35,320

1,968.9

32,420

0.4

0.0

2.7

11.0

250

52

..

..

..

–0.3

..

127.8

377.9

351

91

5,739.5

44,900

4,516.3

35,330

–0.7

–1.0

..b

6.2

89.3

70

83

27.1

4,380

36.6

5,930

2.6

0.4

Kazakhstan

16.6

2,724.9

6

54

136.7

8,260

186.4

11,250

7.5

6.0

Kenya

41.6

580.4

73

24

34.1

820

71.1

Kiribati

0.1

0.8

125

44

0.2

2,030

Korea, Dem. Rep.

24.5

120.5

203

60

..

Korea, Rep.

49.8

99.9

513

83

1,039.0

Kosovo

1.8

10.9

166

..

6.3

3,510

..

Kuwait

2.8

17.8

158

98

133.8

48,900

147.0

Kyrgyz Republic

5.5

199.9

29

35

5.0

900

12.7

2,290

6.0

4.7

Lao PDR

6.3

236.8

27

34

7.1

1,130

16.2

2,580

8.0

6.5

Latvia

2.1

64.6

33

68

27.4

13,320 d

39.3

19,090

5.5

14.7

Lebanon

4.3

10.5

416

87

38.9

9,140

61.6

14,470

3.0

2.2

Lesotho

2.2

30.4

72

28

2.7

1,210

4.5

2,050

4.2

3.1

Liberia

4.1

111.4

43

48

1.4

330

2.2

540

9.4

5.9

Libya

6.4

1,759.5

4

78

77.1

12,320

Liechtenstein

0.0 g

0.2

227

14

4.9

137,070

..k 20,870

0.3a

1,710

4.4

1.6

3,300a

1.8

0.2

..

..

..

..

1,511.7

30,370

3.6

2.9

..

5.0

3.4

53,720

8.2

5.1

105.2a ..

16,800a

2.1

0.3

..

–1.2

–1.9

Lithuania

3.0

65.3

48

67

39.3

12,980 d

62.9

20,760

5.9

14.8

Luxembourg

0.5

2.6

200

85

40.1

77,390

33.2

64,110

1.7

–0.6

Macedonia, FYR

2.1

25.7

82

59

9.9

4,810

23.5

11,370

2.8

2.7

21.3

587.0

37

33

9.1

430

20.2

950

1.0

–1.9

Madagascar Malawi

15.4

118.5

163

16

5.6

360

13.4

870

4.3

1.1

Malaysia

28.9

330.8

88

73

253.0

8,770

451.7

15,650

5.1

3.4

0.3

0.3

1,067

41

1.8

5,720

2.4

7,430

7.5

6.1

15.8

1,240.2

13

35

9.7

610

16.8

1,060

2.7

–0.3

Maldives Mali Malta

0.4

0.3

1,299

95

7.7

18,620

10.2

24,480

2.1

2.2

Marshall Islands

0.1

0.2

305

72

0.2

3,910

..

..

5.0

3.5

Mauritania

3.5

1,030.7

3

42

3.6

1,030l

8.9

2,530

4.0

1.6

Mauritius

1.3

2.0

634

42

10.3

8,040

18.4

14,330

4.1

3.7

114.8

1,964.4

59

78

1,081.8

9,420

1,766.4

15,390

3.9

2.7

Micronesia, Fed. Sts.

0.1

0.7

159

23

0.3

2,860

0.4 a

3,580a

2.1

1.6

Moldova

3.6m

1,980m

13.0m

3,640m

Monaco

0.0 g

Mongolia

2.8

1,564.1

Montenegro

0.6

13.8

32.3

446.6

72

Mozambique

23.9

799.4

Myanmar

48.3

676.6

2.3

824.3

Nepal

30.5

147.2

213

17

16.6

540

Netherlands

16.7

41.5

495

83

829.0

49,660

Mexico

Morocco

Namibia

33.9 0.0e

124m

48m

17,714

7.1m

100

6.5

183,150

..

2

69

6.5

2,310

47

63

4.5

7,140

57

97.6n

2,970n

160.1n

30

31

11.1

460

74

33

..

3

38

10.9

..k 4,700

..c

New Caledonia

0.3

18.6

14

62

..

New Zealand

4.4

267.7

17

86

127.3

29,140

Nicaragua Niger

22 

Surface area

World Development Indicators 2013

5.9

130.4

49

58

8.9

1,510

16.1

1,267.0

13

18

5.8

360

Front

?

User guide

6.4m

6.5m

..

–2.6

–2.7

12.0

4,290

17.5

15.7

8.7

13,700

3.2

3.1

4,880n

4.5n

3.5n

22.9

960

7.1

4.7

..

..

..

..

15.4

6,610

4.8

3.0

38.4

1,260

3.9

2.1

720.3

43,150

1.0

0.5

..

..

..

..

126.3

28,930

1.0

0.1

21.9a

3,730a

5.1

3.6

11.6

720

2.3

–1.2

World view People Environment


World view  1 Population

Surface area

Population density

Urban population

Gross national income

millions

thousand sq. km

people per sq. km

% of total population

$ billions

2011

2011

2011

2011

Atlas method

Purchasing power parity

Gross domestic product

Per capita $

$ billions

Per capita $

% growth

Per capita % growth

2011

2011

2011

2011

2010–11

2010–11

162.5

923.8

178

50

207.3

1,280

372.8

2,290

7.4

4.7

Northern Mariana Islands

0.1

0.5

133

92

..

..

..

..

..

Norway

5.0

323.8

16

79

440.2

88,870

304.4

61,450

1.4

0.1

2.8

309.5

9

73

53.6

19,260

71.6

25,720

5.5

3.1

176.7

796.1

229

36

198.0

1,120

507.2

2,870

3.0

1.1

Nigeria

Oman Pakistan

..c

Palau

0.0 g

0.5

45

84

0.1

6,510

0.2a

11,080a

5.8

5.1

Panama

3.6

75.4

48

75

26.7

7,470

51.8a

14,510a

10.6

8.9 6.6

Papua New Guinea

7.0

462.8

16

13

10.4

1,480

18.0a

2,570a

9.0

Paraguay

6.6

406.8

17

62

19.8

3,020

35.4

5,390

6.9

5.0

29.4

1,285.2

23

77

151.4

5,150

277.6

9,440

6.8

5.6

Philippines

94.9

300.0

318

49

209.7

2,210

393.0

4,140

3.9

2.2

Poland

38.5

312.7

127

61

477.0

12,380o

780.8

20,260

4.3

3.4

Portugal

10.6

92.1

115

61

225.6

21,370

259.9

24,620

–1.7

–0.9

Peru

Puerto Rico

3.7

8.9

418

99

61.6

16,560

..

..

–2.1

–1.6

Qatar

1.9

11.6

161

99

150.4

80,440

161.6

86,440

18.8

11.7

Romania

21.4

238.4

93

53

174.0

8,140

337.4

15,780

2.5

2.7

143.0

17,098.2

9

74

1,522.3

10,650

2,917.7

20,410

4.3

3.9

Rwanda

10.9

26.3

444

19

6.2

570

13.9

1,270

8.3

5.1

Samoa

0.2

2.8

65

20

0.6

3,160

4,270a

2.0

1.6

San Marino

0.0 g

0.1

529

94

..

..

..

..

..

São Tomé and Príncipe

0.2

1.0

176

63

0.2

1,350

0.4

2,080

4.9

3.0

Russian Federation

0.8a

..c

Saudi Arabia

28.1

2,149.7p

13

82

500.5

17,820

693.7

24,700

6.8

4.4

Senegal

12.8

196.7

66

43

13.7

1,070

24.8

1,940

2.6

–0.1

Serbia

7.3

88.4

83

56

41.3

5,690

83.8

Seychelles

0.1

0.5

187

54

1.0

11,270

Sierra Leone

6.0

71.7

84

39

2.8

Singapore

5.2

0.7

7,405

100

222.6

Sint Maarten

0.0 g

0.0e

1,077

..

..

Slovak Republic

5.4

49.0

112

55

87.4

Slovenia

2.1

20.3

102

50

Solomon Islands

0.6

28.9

20

21

Somalia

9.6

637.7

15

38

..

50.6

1,219.1

42

62

352.0

South Africa

11,550

2.0

2.5

2.3a

26,280a

5.0

5.6

460

6.8

1,140

6.0

3.7

42,930

307.8

59,380

4.9

2.7

..

..

..

..

16,190

120.4

22,300

3.3

4.0

48.5

23,600

54.4

26,500

–0.2

–0.4

0.6

1,110

9.0

6.2

..c

..k 6,960

2,350a

..

..

..

..

542.0

10,710

3.1

1.9

South Sudan

10.3p

644.3

..

18

..

..

..

1.9

–1.7

Spain

46.2

505.4

93

77

1,428.3

30,930

1,451.7

31,440

0.4

0.2

Sri Lanka

20.9

65.6

333

15

53.8

2,580

115.2

5,520

8.3

7.1

St. Kitts and Nevis

0.1

0.3

204

32

0.7

12,610

0.9a

16,470a

2.1

0.9

St. Lucia

0.2

0.6

289

18

1.2

6,820

2.0a

11,220a

1.3

0.1

g

St. Martin

0.0

St. Vincent and Grenadines

0.1 34.3p,r

Sudan

..q

1.3a

c

0.1

563

..

..

..

..

..

..

0.4

280

49

0.7

6,070

1.1a

..

10,440a

0.1

0.1

1,861.5r

18

33

58.3u

1,310u

94.7u

2,120u

4.7u

2.2u

a

a

Suriname

0.5

163.8

3

70

4.1

7,840

4.1

Swaziland

1.1

17.4

62

21

3.7

3,470

6.5

Sweden

9.4

450.3

23

85

502.5

53,170

398.9

42,210

3.9

3.1

Switzerland

7.9

41.3

198

74

604.1

76,350

415.6

52,530

1.9

0.8

20.8

185.2

113

56

67.9

2,750

104.0

5,080

..

..

7.0

142.6

50

27

6.1

870

16.0

2,300

7.4

5.9

Syrian Arab Republic Tajikistan

Economy

States and markets

Global links

Back

7,730

6,110

4.7

3.7

1.3

0.1

World Development Indicators 2013 23


1  World view Population

Surface area

Population density

Urban population

Gross national income

millions

thousand sq. km

people per sq. km

% of total population

$ billions

Per capita $

$ billions

2011

2011

2011

2011

2011

2011

2011

Atlas method

Purchasing power parity

Gross domestic product

Per capita $

% growth

Per capita % growth

2011

2010–11

2010–11

6.4v

3.3v –0.5

Tanzania

46.2

947.3

52

27

24.2 v

540 v

67.1v

1,500 v

Thailand

581.4

69.5

513.1

136

34

308.3

4,440

Timor-Leste

1.2

14.9

79

28

3.1

2,730

Togo

6.2

56.8

113

38

3.5

570

Tonga

0.1

0.8

145

24

0.4

3,820

0.5a

5,000a

4.9

4.5

Trinidad and Tobago

1.3

5.1

262

14

21.3

15,840

32.7a

24,350a

–4.1

–4.4

96.0

8,990

–2.0

–3.1

1,247.3

16,940

8.5

7.2

14.7

13.3

4,020 d

Tunisia

10.7

163.6

69

66

42.9

Turkey

73.6

783.6

96

71

766.6

10,410

5.1

488.1

11

49

24.5

4,800

Turks and Caicos Islands

0.0 g

1.0

41

94

..

Tuvalu

0.0 g

0.0e

328

51

0.0

Uganda

34.5

241.6

173

16

17.5

Ukraine

45.7

603.6

79

69

142.9

7.9

83.6

94

84

321.7

40,760

Turkmenistan

United Arab Emirates United Kingdom United States Uruguay

..c

8,360

0.1

5.9a

5,200a

10.6

7.5

6.4

1,040

4.9

2.7

44.3a

8,690a

..

..

..

..

..

..

1.2

1.0

510

45.3

1,310

6.7

3.3

3,130

321.9

7,040

5.2

5.6

377.9

47,890

4.9

–0.1

4,950

62.7

243.6

259

80

2,370.4

37,780

2,255.9

35,950

0.8

0.0

311.6

9,831.5

34

82

15,148.2

48,620

15,211.3

48,820

1.7

1.0

49.3

14,640

5.7

5.3

3.4

176.2

19

93

40.0

11,860

29.3

447.4

69

36

44.2

1,510

100.3a

3,420a

8.3

5.4

0.2

12.2

20

25

0.7

2,730

1.1a

4,330a

1.4

–1.0

Venezuela, RB

29.3

912.1

33

94

346.1

11,820

363.9

12,430

4.2

2.6

Vietnam

87.8

331.1

283

31

111.1

1,270

285.5

3,250

5.9

4.8 ..

Uzbekistan Vanuatu

Virgin Islands (U.S.)

0.1

0.4

313

95

..

..c

..

..

..

West Bank and Gaza

3.9

6.0

652

74

..

..q

..

..

..

..

24.8

528.0

47

32

26.4

1,070

53.7

2,170

–10.5

–13.2 2.1

Yemen, Rep. Zambia

13.5

752.6

18

39

15.7

1,160

20.1

1,490

6.5

Zimbabwe

12.8

390.8

33

39

8.4

660

..

..

9.4

7.8

54 w

52 w

2.7 w

1.6 w

World Low income Middle income

6,974.2 s

134,269.2 s

816.8

16,582.1

51

28

466.0

5,022.4

81,875.7

63

50

20,835.4

66,354.3

t

9,514 w

80,624.2 t

11,560 w

571

1,125.4

1,378

6.0

3.7

4,149

36,311.9

7,230

6.3

5.2

Lower middle income

2,532.7

20,841.9

126

39

4,488.5

1,772

9,719.0

3,837

5.5

3.9

Upper middle income

2,489.7

61,033.8

42

61

16,340.5

6,563

26,646.2

10,703

6.6

5.9

Low & middle income

5,839.2

98,457.7

61

47

21,324.4

3,652

37,436.4

6,411

6.3

5.0

East Asia & Pacific

1,974.2

16,301.7

125

49

8,387.3

4,248

14,344.6

7,266

8.3

7.6

408.1

23,613.7

18

65

3,156.6

7,734

5,845.7

14,323

5.9

5.4

Latin America & Carib.

589.0

20,393.6

29

79

5,050.3

8,574

6,822.0

11,582

4.7

3.6

Middle East & N. Africa

336.5

8,775.4

39

59

1,279.5

3,866

2,619.2

8,052

0.2

2.4

1,656.5

5,131.1

347

31

2,174.5

1,313

5,523.5

3,335

6.1

4.6 2.1

Europe & Central Asia

South Asia Sub-­Saharan Africa High income Euro area

874.8

24,242.3

37

37

1,100.8

1,258

1,946.2

2,225

4.7

1,135.0

35,811.5

33

81

45,242.5

39,861

43,724.5

38,523

1.5

0.9

332.9

2,628.4

131

76

12,871.5

38,661

11,735.7

35,250

1.5

1.2

a. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. b. Estimated to be upper middle income ($4,036–$12,475). c. Estimated to be high income ($12,476 or more). d. Included in the aggregates for upper middle-income economies based on earlier data. e. Greater than 0 but less than 50. f. Data are for the area controlled by the government of Cyprus. g. Greater than 0 but less than 50,000. h. Excludes Abkhazia and South Ossetia. i. Refers to area free from ice. j. Greater than 0 but less than 0.5. k. Estimated to be low income ($1,025 or less). l. Included in the aggregates for low-income economies based on earlier data. m. Excludes Transnistria. n. Includes Former Spanish Sahara. o. Included in the aggregates for high-income economies based on earlier data. p. Provisional estimate. q. Estimated to be lower middle income ($1,026–$4,035). r. Excludes South Sudan. s. Sum of available data (see Statistical methods). t. Missing data are imputed (see Statistical methods). u. Excludes South Sudan after July 9, 2011. v. Covers mainland Tanzania only. w. Weighted average (see Statistical methods).

24 

World Development Indicators 2013

Front

?

User guide

World view People Environment


World view  1 About the data Population, land area, income (as measured by gross national

area, which excludes bodies of water, and from gross area, which

income, GNI), and output (as measured by gross domestic product,

may include offshore territorial waters. Land area is particularly

GDP) are basic measures of the size of an economy. They also pro-

important for understanding an economy’s agricultural capacity and

vide a broad indication of actual and potential resources and are

the environmental effects of human activity. Innovations in satellite

therefore used throughout World Development Indicators to normal-

mapping and computer databases have resulted in more precise

ize other indicators.

measurements of land and water areas.

Population

Urban population

Population estimates are usually based on national population cen-

There is no consistent and universally accepted standard for

suses. Estimates for the years before and after the census are

distinguishing urban from rural areas, in part because of the

interpolations or extrapolations based on demographic models.

wide variety of situations across countries. Most countries use

Errors and undercounting occur even in high-income countries; in

an urban classification related to the size or characteristics

developing countries errors may be substantial because of limits

of settlements. Some define urban areas based on the pres-

in the transport, communications, and other resources required to

ence of certain infrastructure and services. And other countries

conduct and analyze a full census.

designate urban areas based on administrative arrangements.

The quality and reliability of official demographic data are also

Because the estimates in the table are based on national defi-

affected by public trust in the government, government commit-

nitions of what constitutes a city or metropolitan area, cross-

ment to full and accurate enumeration, confidentiality and pro-

country comparisons should be made with caution. To estimate

tection against misuse of census data, and census agencies’

urban populations, ratios of urban to total population obtained

independence from political influence. Moreover, comparability of

from the United Nations were applied to the World Bank’s esti-

population indicators is limited by differences in the concepts,

mates of total population.

definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that col-

Size of the economy

lect the data.

GNI measures total domestic and foreign value added claimed by

Of the 214 economies in the table, 180 (about 86 percent) con-

residents. GNI comprises GDP plus net receipts of primary income

ducted a census during the 2000 census round (1995–2004). As

(compensation of employees and property income) from nonresi-

of January 2012, 141 countries have completed a census for the

dent sources. GDP is the sum of gross value added by all resident

2010 census round (2005–14). The currentness of a census and

producers in the economy plus any product taxes (less subsidies)

the availability of complementary data from surveys or registration

not included in the valuation of output. GNI is calculated without

systems are important indicators of demographic data quality. See

deducting for depreciation of fabricated assets or for depletion and

Primary data documentation for the most recent census or survey

degradation of natural resources. Value added is the net output of

year and for the completeness of registration. Some European coun-

an industry after adding up all outputs and subtracting intermedi-

tries’ registration systems offer complete information on population

ate inputs. The industrial origin of value added is determined by

in the absence of a census.

the International Standard Industrial Classification revision 3. The

Current population estimates for developing countries that

World Bank uses GNI per capita in U.S. dollars to classify countries

lack recent census data and pre- and post-census estimates for

for analytical purposes and to determine borrowing eligibility. For

countries with census data are provided by the United Nations

definitions of the income groups in World Development Indicators,

Population Division and other agencies. The cohort compo-

see User guide.

nent method—a standard method for estimating and projecting

When calculating GNI in U.S. dollars from GNI reported in national

­p opulation—requires fertility, mortality, and net migration data,

currencies, the World Bank follows the World Bank Atlas conversion

often collected from sample surveys, which can be small or limited

method, using a three-year average of exchange rates to smooth

in coverage. Population estimates are from demographic modeling

the effects of transitory fluctuations in exchange rates. (For fur-

and so are susceptible to biases and errors from shortcomings in

ther discussion of the World Bank Atlas method, see Statistical

the model and in the data. Because the five-year age group is the

methods.)

cohort unit and five-year period data are used, interpolations to

Because exchange rates do not always reflect differences in price

obtain annual data or single age structure may not reflect actual

levels between countries, the table also converts GNI and GNI per

events or age composition.

capita estimates into international dollars using purchasing power parity (PPP) rates. PPP rates provide a standard measure allowing

Surface area

comparison of real levels of expenditure between countries, just

The surface area of an economy includes inland bodies of water

as conventional price indexes allow comparison of real values over

and some coastal waterways. Surface area thus differs from land

time.

Economy

States and markets

Global links

Back

World Development Indicators 2013 25


1  World view PPP rates are calculated by simultaneously comparing the prices

Data sources

of similar goods and services among a large number of countries.

The World Bank’s population estimates are compiled and produced

In the most recent round of price surveys conducted by the Interna-

by its Development Data Group in consultation with its Human Devel-

tional Comparison Program (ICP) in 2005, 146 countries and ter-

opment Network, operational staff, and country offices. The United

ritories participated, including China for the first time, India for the

Nations Population Division (2011) is a source of the demographic

first time since 1985, and almost all African countries. The PPP

data for more than half the countries, most of them developing

conversion factors presented in the table come from three sources.

countries. Other important sources are census reports and other

For 47 high- and upper middle-income countries conversion fac-

statistical publications from national statistical offices; household

tors are provided by Eurostat and the Organisation for Economic

surveys by national agencies, ICF International (for MEASURE DHS),

Co-operation and Development (OECD); PPP estimates for these

and the U.S. Centers for Disease Control and Prevention; Eurostat’s

countries incorporate new price data collected since 2005. For the

Demographic Statistics; the Secretariat of the Pacific Community’s

remaining 2005 ICP countries the PPP estimates are extrapolated

Statistics and Demography Programme; and the U.S. Bureau of the

from the 2005 ICP benchmark results, which account for relative

Census’s International Data Base.

price changes between each economy and the United States. For

Data on surface and land area are from the Food and Agricul-

countries that did not participate in the 2005 ICP round, the PPP

ture Organization, which gathers these data from national agen-

estimates are imputed using a statistical model. More information

cies through annual questionnaires and by analyzing the results of

on the results of the 2005 ICP is available at www.worldbank.org/

national agricultural censuses.

data/icp.

Data on urban population shares are from United Nations Popula-

Growth rates of GDP and GDP per capita are calculated using the

tion Division (2010).

least squares method and constant price data in local currency.

GNI, GNI per capita, GDP growth, and GDP per capita growth are

Constant price U.S. dollar series are used to calculate regional and

estimated by World Bank staff based on national accounts data col-

income group growth rates. The growth rates in the table are annual

lected by World Bank staff during economic missions or reported by

averages. Methods of computing growth rates are described in Sta-

national statistical offices to other international organizations such

tistical methods.

as the OECD. PPP conversion factors are estimates by Eurostat/ OECD and by World Bank staff based on data collected by the ICP.

Definitions • Population is based on the de facto definition of population, which

References

counts all residents regardless of legal status or ­citizenship—

Eurostat (Statistical Office of the European Communities). n.d. Demo-

except for refugees not permanently settled in the country of asy-

graphic Statistics. http://epp.eurostat.ec.europa.eu/portal/page/

lum, who are generally considered part of the population of their country of origin. The values shown are midyear estimates. • Surface area is a country’s total area, including areas under inland bodies of water and some coastal waterways. • Population density is midyear population divided by land area. • Urban population is

http://www.measuredhs.com. Calverton, MD. OECD (Organisation for Economic Co-operation and Development). n.d. OECD.StatExtracts database. http://stats.oecd.org/. Paris.

the midyear population of areas defined as urban in each country

UNAIDS (Joint United Nations Programme on HIV/AIDS). 2012. Global

and reported to the United Nations. • Gross national income, Atlas

Report: UNAIDS Report on the Global AIDS Epidemic 2012. www

method, is the sum of value added by all resident producers plus

.unaids.org/en/resources/publications/2012/. Geneva.

any product taxes (less subsidies) not included in the valuation

United Nations Inter-Agency Group for Child Mortality Estimation.

of output plus net receipts of primary income (compensation of

2012. Levels and Trends in Child Mortality: Report 2012. www

employees and property income) from abroad. Data are in current

.childinfo.org/files/Child_Mortality_Report_2012.pdf. New York.

U.S. dollars converted using the World Bank Atlas method (see

United Nations. 2012. The Millennium Development Goals Report

Statistical methods). • Gross national income, purchasing power

2012. New York.

parity, is GNI converted to international dollars using PPP rates. An

United Nations Population Division. 2010. World Urbanization Pros-

international dollar has the same purchasing power over GNI that

pects: The 2009 Revision. New York: United Nations, Department of

a U.S. dollar has in the United States. • Gross national income per capita is GNI divided by midyear population. • Gross domestic

Economic and Social Affairs. ———. 2011. World Population Prospects: The 2010 Revision. New

product is the sum of value added by all resident producers plus

York: United Nations, Department of Economic and Social Affairs.

any product taxes (less subsidies) not included in the valuation of

World Bank. 2011. The Changing Wealth of Nations: Measuring Sustain-

output. Growth is calculated from constant price GDP data in local currency. • Gross domestic product per capita is GDP divided by midyear population.

26 

portal/eurostat/home/. Luxembourg. ICF International. Various years. Demographic and Health Surveys.

World Development Indicators 2013

able Development for the New Millennium. Washington, DC. ———. n.d. PovcalNet online database. http://iresearch.worldbank .org/PovcalNet/index.htm

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User guide

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World view  1 About the Online tables dataand indicators To access the World Development Indicators online tables, use

indicator online, use the URL http://data.worldbank.org/indicator/

the URL http://wdi.worldbank.org/table/ and the table number (for

and the indicator code (for example, http://data.worldbank.org/

example, http://wdi.worldbank.org/table/1.1). To view a specific

indicator/SP.POP.TOTL).

1.1 Size of the economy

Carbon dioxide emissions per capita

Population

SP.POP.TOTL

Surface area

AG.SRF.TOTL.K2

Population density

EN.POP.DNST

Nationally protected terrestrial and marine areas Access to improved sanitation facilities

EN.ATM.CO2E.PC ER.PTD.TOTL.ZS SH.STA.ACSN ..a

Gross national income, Atlas method

NY.GNP.ATLS.CD

Individuals using the Internet

Gross national income per capita, Atlas method

NY.GNP.PCAP.CD

1.4 Millennium Development Goals: overcoming obstacles

Purchasing power parity gross national income

NY.GNP.MKTP.PP.CD

Purchasing power parity gross national income, Per capita

NY.GNP.PCAP.PP.CD

Gross domestic product

NY.GDP.MKTP.KD.ZG

Gross domestic product, Per capita

NY.GDP.PCAP.KD.ZG

1.2 Millennium Development Goals: eradicating poverty and saving lives

This table provides data on net official development assistance by donor, least developed countries’ access to high-income markets, and the Debt Initiative for Heavily Indebted Poor Countries.

1.5 Women in development Female population

SP.POP.TOTL.FE.ZS

Life expectancy at birth, Male

SP.DYN.LE00.MA.IN

Life expectancy at birth, Female

Share of poorest quintile in national consumption or income

SI.DST.FRST.20

SH.STA.ANVC.ZS SP.MTR.1519.ZS

Vulnerable employment

SL.EMP.VULN.ZS

Prevalence of malnutrition, Underweight

SH.STA.MALN.ZS

Primary completion rate

SE.PRM.CMPT.ZS

Women in wage employment in nonagricultural sector

Under-five mortality rate

SP.DYN.LE00.FE.IN

Pregnant women receiving prenatal care Teenage mothers

Ratio of girls to boys enrollments in primary and secondary education

..a

Unpaid family workers, Male

SL.EMP.INSV.FE.ZS SL.FAM.WORK.MA.ZS

SE.ENR.PRSC.FM.ZS

Unpaid family workers, Female

SL.FAM.WORK.FE.ZS

SH.DYN.MORT

Female part-time employment

SL.TLF.PART.TL.FE.ZS

1.3 Millennium Development Goals: protecting our

Female legislators, senior officials, and managers

common environment

Women in parliaments

SG.GEN.PARL.ZS

Female-headed households

SP.HOU.FEMA.ZS

Maternal mortality ratio, Modeled estimate

SH.STA.MMRT

Contraceptive prevalence rate

SP.DYN.CONU.ZS

HIV prevalence

SH.DYN.AIDS.ZS

Incidence of tuberculosis

Economy

SH.TBS.INCD

States and markets

SG.GEN.LSOM.ZS

Data disaggregated by sex are available in the World Development Indicators database. a. Available online only as part of the table, not as an individual indicator.

Global links

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World Development Indicators 2013 27


Poverty rates Population below international poverty linesa

International poverty line in local currency

Poverty gap at $2 a day %

Population Poverty gap Population below at $1.25 below $1.25 a day a day $2 a day % % %

Poverty gap at $2 a day %

$2 a day

2005

2005

Survey year b

120.8

2005

<2

<0.5

7.9

1.5

2008

<2

<0.5

4.3

0.9

1988

7.6

1.2

24.6

6.7

1995

6.8

1.4

23.6

6.5

..

..

..

..

2000

54.3

29.9

70.2

42.4

2009d,e

2.0

1.2

3.4

1.7

2010 d,e

<2

0.7

<2

0.9 4.0

Survey year b

Albania

75.5

Algeria

48.4 c

Angola

88.1

141.0

1.7

2.7

245.2

392.4

2008

<2

<0.5

12.4

2.3

2010

2.5

<0.5

19.9

2,170.9

3,473.5

2001

6.3

1.1

27.1

6.8

2008

<2

<0.5

2.8

0.6

31.9

51.0

2005

50.5

14.2

80.3

34.3

2010

43.3

11.2

76.5

30.4

949.5

1,519.2

2008

<2

<0.5

<2

<0.5

2010

<2

<0.5

<2

<0.5

1998f

11.3

4.7

26.3

10.0

1999 f

12.2

5.5

22.0

9.9

..

..

..

..

2003

47.3

15.7

75.3

33.5

Argentina Armenia Azerbaijan Bangladesh Belarus Belize Benin

1.8 c

77.5c

2.9c

344.0

550.4

Bhutan

23.1

36.9

2003

26.2

7.0

49.5

18.8

2007

10.2

1.8

29.8

8.5

Bolivia

3.2

5.1

2007e

13.1

6.6

24.7

10.9

2008e

15.6

8.6

24.9

13.1

Bosnia and Herzegovina

1.1

1.7

2004

<2

<0.5

<2

<0.5

2007

<2

<0.5

<2

<0.5

Botswana

4.2

6.8

1986

35.6

13.8

54.7

25.8

1994

31.2

11.0

49.4

22.3

Brazil

2.0

3.1

2008f

6.0

3.4

11.3

5.3

2009 f

6.1

3.6

10.8

5.4

Bulgaria

0.9

1.5

2003

<2

<0.5

<2

<0.5

2007

<2

<0.5

<2

<0.5

Burkina Faso

303.0

484.8

2003

56.5

20.3

81.2

39.3

2009

44.6

14.7

72.6

31.7

Burundi

558.8

894.1

1998

86.4

47.3

95.4

64.1

2006

81.3

36.4

93.5

56.1

Cambodia

2,019.1

3,230.6

2008

22.8

4.9

53.3

17.4

2009

18.6

3.6

49.5

15.1

Cameroon

368.1

589.0

2001

10.8

2.3

32.5

9.5

2007

9.6

1.2

30.4

8.2

97.7

156.3

..

..

..

..

2002

21.0

6.1

40.9

15.2

62.4

28.3

81.9

45.3

2008

62.8

31.3

80.1

46.8

..

..

..

..

2003

61.9

25.6

83.3

43.9 1.2

Cape Verde Central African Republic

384.3

614.9

Chad

409.5

655.1

Chile

484.2

774.7

China

5.1g

2003

8.2g

2006f

<2

0.5

3.2

1.1

2009 f

<2

0.7

2.7

2008h

13.1

3.2

29.8

10.1

2009h

11.8

2.8

27.2

9.1

2009 f

9.7

4.7

18.5

8.2

2010 f

8.2

3.8

15.8

6.8 34.2

Colombia

1,489.7

2,383.5

Comoros

368.0

588.8

..

..

..

..

2004

46.1

20.8

65.0

Congo, Dem. Rep.

395.3

632.5

..

..

..

..

2006

87.7

52.8

95.2

67.6

Congo, Rep.

469.5

751.1

..

..

..

..

2005

54.1

22.8

74.4

38.8

Costa Rica

348.7c

557.9c

2008f

2.4

1.5

5.0

2.3

2009 f

3.1

1.8

6.0

2.7

Côte d’Ivoire

407.3

651.6

2002

23.3

6.8

46.8

17.6

2008

23.8

7.5

46.3

17.8

Croatia Czech Republic Djibouti

5.6

8.9

2004

<2

<0.5

<2

<0.5

2008

<2

<0.5

<2

<0.5

19.0

30.4

1993e

<2

<0.5

<2

<0.5

1996e

<2

<0.5

<2

<0.5

134.8

215.6

..

..

..

..

2002

18.8

5.3

41.2

14.6 2.4

25.5c

40.8 c

2009 f

3.0

0.7

10.0

2.7

2010 f

2.2

<0.5

9.9

Ecuador

0.6

1.0

2009 f

6.4

2.9

13.5

5.5

2010 f

4.6

2.1

10.6

4.1

Egypt, Arab Rep.

2.5

4.0

2005

2.0

<0.5

18.5

3.5

2008

<2

<0.5

15.4

2.8

El Salvador

6.0 c

9.6c

2008f

5.4

1.9

14.0

4.8

2009 f

9.0

4.4

16.9

7.6

2003

<2

<0.5

2.6

<0.5

2004

<2

<0.5

<2

0.5

Dominican Republic

Estonia

11.0

17.7

Ethiopia

3.4

5.5

2005

39.0

9.6

77.6

28.9

2011

30.7

8.2

66.0

23.6

Fiji

1.9

3.1

2003

29.2

11.3

48.7

21.8

2009

5.9

1.1

22.9

6.0

554.7

887.5

..

..

..

..

2005

4.8

0.9

19.6

5.0

12.9

20.7

1998

65.6

33.8

81.2

49.1

2003

29.8

9.8

55.9

24.4

1.0

1.6

2008

15.3

4.6

32.2

11.7

2010

18.0

5.8

35.6

13.7

5,594.8

8,951.6

1998

39.1

14.4

63.3

28.5

2006

28.6

9.9

51.8

21.3

2004f

24.4

13.2

39.2

20.2

2006f

13.5

4.7

26.3

10.5

2003

56.3

21.3

80.8

39.7

2007

43.3

15.0

69.6

31.0

Gabon Gambia, The Georgia Ghana Guatemala Guinea

28 

Population Poverty gap Population below at $1.25 below $1.25 a day a day $2 a day % % %

$1.25 a day

World Development Indicators 2013

5.7c 1,849.5

9.1c 2,959.1

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Poverty rates Population below international poverty linesa

International poverty line in local currency

Population Poverty gap Population below at $1.25 below $1.25 a day a day $2 a day % % %

Poverty gap at $2 a day %

Population Poverty gap Population below at $1.25 below $1.25 a day a day $2 a day % % %

Poverty gap at $2 a day %

$1.25 a day

$2 a day

2005

2005

Guinea-Bissau

355.3

568.6

1993

52.1

20.6

75.7

37.4

2002

48.9

16.6

78.0

Guyana

131.5c

210.3c

1993e

6.9

1.5

17.1

5.4

1998e

8.7

2.8

18.0

6.7

Haiti

24.2c

38.7c

..

..

..

..

2001

61.7

32.3

77.5

46.7

Honduras

12.1c

19.3c

2008f

21.4

11.8

32.6

17.5

2009 f

17.9

9.4

29.8

14.9

2004

<2

<0.5

<2

<0.5

2007

<2

<0.5

<2

<0.5

Hungary

171.9

275.0

Survey year b

Survey year b

34.9

19.5i

31.2i

2005h

41.6

10.5

75.6

29.5

2010h

32.7

7.5

68.7

24.5

Indonesia

5,241.0i

8,385.7i

2009h

20.4

4.1

52.7

16.5

2010h

18.1

3.3

46.1

14.3

Iran, Islamic Rep.

3,393.5

5,429.6

1998

<2

<0.5

8.3

1.8

2005

<2

<0.5

8.0

1.8

799.8

1,279.7

..

..

..

..

2007

2.8

<0.5

21.4

4.4

2004

<2

<0.5

5.4

0.8

India

Iraq Jamaica Jordan

54.2c

86.7c

2002

<2

<0.5

8.5

1.5

0.6

1.0

2008

<2

<0.5

2.1

<0.5

2010

<2

<0.5

<2

<0.5

Kazakhstan

81.2

129.9

2008

<2

<0.5

<2

<0.5

2009

<2

<0.5

<2

<0.5

Kenya

40.9

65.4

1997

19.6

4.6

42.7

14.7

2005

43.4

16.9

67.2

31.8

Kyrgyz Republic

16.2

26.0

2009

6.2

1.4

21.7

6.0

2010

6.7

1.5

22.9

6.4

4,677.0

7,483.2

2002

44.0

12.1

76.9

31.1

2008

33.9

9.0

66.0

24.8

Lao PDR Latvia

0.4

0.7

2008

<2

<0.5

<2

<0.5

2009

<2

<0.5

<2

<0.5

Lesotho

4.3

6.9

1994

46.2

25.6

59.7

36.1

2003

43.4

20.8

62.3

33.1

..

..

..

..

2007

83.8

40.9

94.9

59.6

<2

<0.5

<2

0.5

2008

<2

<0.5

<2

<0.5

Liberia

0.6

1.0

Lithuania

2.1

3.3

2004

Macedonia, FYR Madagascar Malawi

29.5

47.2

2009

<2

<0.5

5.9

0.9

2010

<2

<0.5

6.9

1.2

945.5

1,512.8

2005

67.8

26.5

89.6

46.9

2010

81.3

43.3

92.6

60.1

1998

83.1

46.0

93.5

62.3

2004

73.9

32.3

90.5

51.8

<2

<0.5

2.9

<0.5

2009e

<2

<0.5

2.3

<0.5

71.2

113.8

Malaysia

2.6

4.2

Maldives

358.3

573.5

..

..

..

..

2004

<2

<0.5

12.2

2.5

Mali

362.1

579.4

2006

51.4

18.8

77.1

36.5

2010

50.4

16.4

78.7

35.2

Mauritania

17.7

2007e

157.1

251.3

2004

25.4

7.0

52.6

19.2

2008

23.4

6.8

47.7

Mexico

9.6

15.3

2008

<2

<0.5

5.2

1.3

2010

<2

<0.5

4.5

1.0

Micronesia, Fed. Sts.

0.8 c

..

..

..

..

31.2

16.3

44.7

24.5

Moldova

6.0

9.7

2009

<2

<0.5

7.1

1.2

2010

<2

<0.5

4.4

0.7

Montenegro

0.6

1.0

2008

<2

<0.5

<2

<0.5

2010

<2

<0.5

<2

<0.5

6.9

11.0

2001

6.3

0.9

24.3

6.3

2007

2.5

0.5

14.0

3.2

14,532.1 23,251.4

2003

74.7

35.4

90.0

53.6

2008

59.6

25.1

81.8

42.9

Morocco Mozambique Namibia Nepal Nicaragua Niger

1.3c

2000 d

6.3

10.1

1993e

49.1

24.6

62.2

36.5

2004 e

31.9

9.5

51.1

21.8

33.1

52.9

2003

53.1

18.4

77.3

36.6

2010

24.8

5.6

57.3

19.0

14.6c

2001e

14.4

3.7

34.4

11.5

2005e

11.9

2.4

31.7

9.6

2005

50.2

18.3

75.3

35.6

2008

43.6

12.4

75.2

30.8

9.1c 334.2

534.7

Nigeria

98.2

157.2

2004

63.1

28.7

83.1

45.9

2010

68.0

33.7

84.5

50.2

Pakistan

25.9

41.4

2006

22.6

4.1

61.0

18.8

2008

21.0

3.5

60.2

17.9

2009 f

5.9

1.8

14.6

4.9

2010 f

6.6

2.1

13.8

5.1

..

..

..

..

1996

35.8

12.3

57.4

25.5

Panama

0.8 c

1.2c

Papua New Guinea

2.1c

3.4 c

Paraguay Peru Philippines

4,255.6

2009 f

7.6

3.2

14.2

6.0

2010 f

7.2

3.0

13.2

5.7

2.1

3.3

2009 f

5.5

1.6

14.0

4.6

2010 f

4.9

1.3

12.7

4.1

2,659.7 30.2

48.4

2006

22.6

5.5

45.0

16.4

2009

18.4

3.7

41.5

13.8

Poland

2.7

4.3

2009

<2

<0.5

<2

<0.5

2010

<2

<0.5

<2

<0.5

Romania

2.1

3.4

2009

<2

<0.5

<2

0.5

2010

<2

<0.5

<2

<0.5

16.7

26.8

2008

<2

<0.5

<2

<0.5

2009

<2

<0.5

<2

<0.5

295.9

473.5

2006

72.1

34.8

87.4

52.2

2011

63.2

26.6

82.4

44.6

Russian Federation Rwanda

Economy

States and markets

Global links

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World Development Indicators 2013 29


Poverty rates Population below international poverty linesa

International poverty line in local currency

$1.25 a day

$2 a day

2005

2005

São Tomé and Príncipe Senegal Serbia Seychelles Sierra Leone Slovak Republic Slovenia South Africa Sri Lanka St. Lucia Sudan

Survey year b

Population Poverty gap Population below at $1.25 below $1.25 a day a day $2 a day % % %

7,953.9 12,726.3 372.8

596.5

42.9

68.6

5.6c

9.0 c

Poverty gap at $2 a day %

Survey year b

Population Poverty gap Population below at $1.25 below $1.25 a day a day $2 a day % % %

Poverty gap at $2 a day %

..

..

..

..

2001

28.2

7.9

54.2

20.6

2005

33.5

10.8

60.4

24.7

2011

29.6

9.1

55.2

21.9

2009

<2

<0.5

<2

<0.5

2010

<2

<0.5

<2

<0.5

2000

<2

<0.5

<2

<0.5

2007

<2

<0.5

<2

<0.5

1990

62.8

44.8

75.0

54.0

2003

53.4

20.3

76.1

37.5

<2

<0.5

<2

<0.5

2009e

<2

<0.5

<2

<0.5

1,745.3

2,792.4

23.5

37.7

198.2

317.2

2003

<2

<0.5

<2

<0.5

2004

<2

<0.5

<2

<0.5

5.7

9.1

2006

17.4

3.3

35.7

12.3

2009

13.8

2.3

31.3

10.2

50.0

80.1

2007

7.0

1.0

29.1

7.4

2010

4.1

0.7

23.9

5.4

..

..

..

..

1995

20.9

7.2

40.6

15.5

..

..

..

..

2009

19.8

5.5

44.1

15.4

..

..

..

..

1999

15.5

5.9

27.2

11.7

2001

62.9

29.4

81.0

45.8

2010

40.6

16.0

60.4

29.3 3.3

2.4 c 154.4

2008e

3.8 c 247.0

Suriname

2.3c

3.7c

Swaziland

4.7

7.5

30.8

49.3

..

..

..

..

2004

<2

<0.5

16.9

1.2

1.9

2007

14.7

4.4

37.0

12.2

2009

6.6

1.2

27.7

7.0

Tanzania

603.1

964.9

2000

84.6

41.6

95.3

60.3

2007

67.9

28.1

87.9

47.5

Thailand

21.8

34.9

2009j

<2

<0.5

4.6

0.8

2010j

<2

<0.5

4.1

0.7

352.8

564.5

2006

38.7

11.4

69.3

27.9

2011

28.2

8.8

52.7

20.9 3.9

Syrian Arab Republic Tajikistan

Togo Trinidad and Tobago

5.8 c

9.2c

1988e

<2

<0.5

8.6

1.9

1992e

4.2

1.1

13.5

Tunisia

0.9

1.4

2005

<2

<0.5

8.1

1.8

2010

<2

<0.5

4.3

1.1

Turkey

1.3

2.0

2008

<2

<0.5

4.2

0.7

2010

<2

<0.5

4.7

1.4 18.4

5,961.1c

9,537.7c

1993e

63.5

25.8

85.7

44.9

1998e

24.8

7.0

49.7

Uganda

930.8

1,489.2

2006

51.5

19.1

75.6

36.4

2009

38.0

12.2

64.7

27.4

Ukraine

2.1

3.4

2009

<2

<0.5

<2

<0.5

2010

<2

<0.5

<2

<0.5

Uruguay

19.1

30.6

2009 f

<2

<0.5

<2

<0.5

2010 f

<2

<0.5

<2

<0.5

13.4

8.2

21.9

11.6

2006f

6.6

3.7

12.9

5.9

21.4

5.3

48.1

16.3

2008

16.9

3.8

43.4

13.5

Turkmenistan

Venezuela, RB

1,563.9

2,502.2

2005f

Vietnam

7,399.9 11,839.8

2006

West Bank and Gaza Yemen, Rep. Zambia

2.7c

4.3c

2007

<2

<0.5

2.5

0.5

2009

<2

<0.5

<2

<0.5

113.8

182.1

1998

12.9

3.0

36.4

11.1

2005

17.5

4.2

46.6

14.8

3,537.9

5,660.7

2004

64.3

32.8

81.5

48.3

2006

68.5

37.0

82.6

51.8

a. Based on nominal per capita consumption averages and distributions estimated parametrically from grouped household survey data, unless otherwise noted. b. Refers to the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected. c. Based on purchasing power parity (PPP) dollars imputed using regression. d. Covers urban areas only. e. Based on per capita income averages and distribution data estimated parametrically from grouped household survey data. f. Estimated nonparametrically from nominal income per capita distributions based on unit-record household survey data. g. PPP conversion factor based on urban prices. h. Population-weighted average of urban and rural estimates. i. Based on benchmark national PPP estimate rescaled to account for cost-of-living differences in urban and rural areas. j. Estimated nonparametrically from nominal consumption per capita distributions based on unit-record household survey data.

30 

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Poverty rates Trends in poverty indicators by region, 1990–2015 Region

1990

1993

1996

1999

2002

2005

2008

2010 provisional 2015 projection

Trend, 1990–2010

Share of population living on less than 2005 PPP $1.25 a day (%) 56.2

50.7

35.9

35.6

27.6

17.1

14.3

12.5

5.5

1.9

2.9

3.9

3.8

2.3

1.3

0.5

0.7

0.4

Latin America & Caribbean

12.2

11.4

11.1

11.9

11.9

8.7

6.5

5.5

4.9

Middle East & North Africa

5.8

4.8

4.8

5.0

4.2

3.5

2.7

2.4

2.6

East Asia & Pacific Europe & Central Asia

South Asia

53.8

51.7

48.6

45.1

44.3

39.4

36.0

31.0

23.2

Sub-­Saharan Africa

56.5

59.4

58.1

57.9

55.7

52.3

49.2

48.5

42.3

Total

43.1

41.0

34.8

34.1

30.8

25.1

22.7

20.6

15.5

People living on less than 2005 PPP $1.25 a day (millions) 926

871

640

656

523

332

284

251

115

9

14

18

18

11

6

2

3

2

Latin America & Caribbean

53

53

54

60

63

48

37

32

30

Middle East & North Africa

13

12

12

14

12

10

9

8

9

South Asia

617

632

631

619

640

598

571

507

406

Sub-­Saharan Africa

290

330

349

376

390

395

399

414

408

1,908

1,910

1,704

1,743

1,639

1,389

1,302

1,215

970

East Asia & Pacific Europe & Central Asia

Total

Regional distribution of people living on less than $1.25 a day (% of total population living on less than $1.25 a day) 48.5

45.6

37.6

37.6

31.9

23.9

21.8

20.7

11.8

Europe & Central Asia

0.5

0.7

1.1

1.0

0.6

0.5

0.2

0.3

0.2

Latin America & Caribbean

2.8

2.7

3.1

3.4

3.8

3.4

2.9

2.7

3.1

Middle East & North Africa

0.7

0.6

0.7

0.8

0.7

0.8

0.7

0.7

1.0

South Asia

32.3

33.1

37.0

35.5

39.1

43.1

43.8

41.7

41.9

Sub-­Saharan Africa

15.2

17.3

20.5

21.6

23.8

28.4

30.7

34.1

42.1

East Asia & Pacific

Average daily consumption or income of people living on less than 2005 PPP $1.25 a day (2005 PPP $) East Asia & Pacific

0.83

0.85

0.89

0.87

0.89

0.95

0.95

0.97

..

Europe & Central Asia

0.90

0.90

0.91

0.92

0.93

0.88

0.88

0.85

..

Latin America & Caribbean

0.71

0.69

0.68

0.67

0.65

0.63

0.62

0.60

..

Middle East & North Africa

1.02

1.02

1.01

1.01

1.01

0.99

0.97

0.96

..

South Asia

0.88

0.89

0.91

0.91

0.92

0.94

0.95

0.96

..

Sub-­Saharan Africa

0.69

0.68

0.69

0.69

0.70

0.71

0.71

0.71

..

Total

0.82

0.83

0.85

0.84

0.85

0.87

0.87

0.87

..

Survey coverage (% of total population represented by underlying survey data) East Asia & Pacific

92.4

93.3

93.7

93.4

93.5

93.2

93.6

93.5

..

Europe & Central Asia

81.5

87.3

97.1

93.9

96.3

94.7

89.9

85.3

..

Latin America & Caribbean

94.9

91.8

95.9

97.7

97.5

95.9

94.5

86.9

..

Middle East & North Africa

76.8

65.3

81.7

70.0

21.5

85.7

46.7

30.8

..

South Asia

96.5

98.2

98.1

20.1

98.0

98.0

97.9

94.5

..

Sub-­Saharan Africa

46.0

68.8

68.0

53.1

65.7

82.7

81.7

64.1

..

Total

86.4

89.4

91.6

68.2

87.8

93.0

90.2

84.7

..

Source: World Bank PovcalNet.

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Poverty rates About the data The World Bank produced its first global poverty estimates for

The low frequency and lack of comparability of the data available

developing countries for World Development Report 1990: Poverty

in some countries create uncertainty over the magnitude of poverty

(World Bank 1990) using household survey data for 22 coun-

reduction. The table on trends in poverty indicators reports the

tries (Ravallion, Datt, and van de Walle 1991). Since then there

percentage of the regional and global population represented by

has been considerable expansion in the number of countries

household survey samples collected during the reference year or

that field household income and expenditure surveys. The World

during the two preceding or two subsequent years (in other words,

Bank’s Development Research Group maintains a database that

within a five-year window centered on the reference year). Data cov-

is updated regularly as new survey data become available (and

erage in Sub-­Saharan Africa and the Middle East and North Africa

thus may contain more recent data or revisions that are not incor-

remains low and variable. The need to improve household survey

porated into the table) and conducts a major reassessment of

programs for monitoring poverty is clearly urgent. But institutional,

progress against poverty about every three years.

political, and financial obstacles continue to limit data collection,

This year the database has been updated with global and regional

analysis, and public access.

extreme poverty estimates for 2010, which are provisional due to low coverage in household survey data availability for recent years

Data quality

(2008–12). The projections to 2015 of poverty rates use the 2010

Besides the frequency and timeliness of survey data, other data

provisional estimates as a baseline and assume that average house-

quality issues arise in measuring household living standards. The

hold income or consumption will grow in line with the aggregate eco-

surveys ask detailed questions on sources of income and how it

nomic projections reported in this year’s Global Monitoring Report

was spent, which must be carefully recorded by trained personnel.

(World Bank 2013) but that inequality within countries will remain

Income is generally more difficult to measure accurately, and con-

unchanged. This methodology was first used and described in the

sumption comes closer to the notion of living standards. And income

Global Economic Prospects report (World Bank 2000). Estimates of

can vary over time even if living standards do not. But consumption

the number of people living in extreme poverty are based on popula-

data are not always available: the latest estimates reported here

tion projections in the World Bank’s HealthStats database (http://

use consumption for about two-thirds of countries.

datatopics.worldbank.org/hnp).

However, even similar surveys may not be strictly comparable

PovcalNet (http://iresearch.worldbank.org/PovcalNet) is an

because of differences in timing, sampling frames, or the quality

interactive computational tool that allows users to replicate these

and training of enumerators. Comparisons of countries at different

internationally comparable $1.25 and $2 a day global, regional and

levels of development also pose a potential problem because of dif-

country-level poverty estimates and to compute poverty measures

ferences in the relative importance of the consumption of nonmarket

for custom country groupings and for different poverty lines. The

goods. The local market value of all consumption in kind (includ-

Poverty and Equity Data portal (http://povertydata.worldbank.org/

ing own production, particularly important in underdeveloped rural

poverty/home) provides access to the database and user-friendly

economies) should be included in total consumption expenditure, but

dashboards with graphs and interactive maps that visualize trends in

may not be. Most survey data now include valuations for consump-

key poverty and inequality indicators for different regions and coun-

tion or income from own production, but valuation methods vary.

tries. The country dashboards display trends in poverty measures

The statistics reported here are based on consumption data or,

based on the national poverty lines (see online table 2.7) alongside

when unavailable, on income data. Analysis of some 20 countries

the internationally comparable estimates in the table, produced

for which both were available from the same surveys found income

from and consistent with PovcalNet.

to yield a higher mean than consumption but also higher inequality. When poverty measures based on consumption and income were

Data availability

compared, the two effects roughly cancelled each other out: there

The World Bank’s internationally comparable poverty monitoring

was no significant statistical difference.

database now draws on income or detailed consumption data

Invariably some sampled households do not participate in

collected from interviews with 1.23 million randomly sampled

surveys because they refuse to do so or because nobody is

households through more than 850 household surveys collected

at home during the interview visit. This is referred to as “unit

by national statistical offices in nearly 130 countries. Despite prog-

nonresponse” and is distinct from “item nonresponse,” which

ress in the last decade, the challenges of measuring poverty remain.

occurs when some of the sampled respondents participate but

The timeliness, frequency, quality, and comparability of household

refuse to answer certain questions, such as those pertaining to

surveys need to increase substantially, particularly in the poorest

income or consumption. To the extent that survey nonresponse

countries. The availability and quality of poverty monitoring data

is random, there is no concern regarding biases in survey-based

remain low in small states, fragile situations, and low-income coun-

inferences; the sample will still be representative of the popula-

tries and even some middle-income countries.

tion. However, households with different income might not be equally likely to respond. Richer households may be less likely

32 

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Poverty rates to participate because of the high opportunity cost of their time

Definitions

or concerns about intrusion in their affairs. It is conceivable that

• International poverty line in local currency is the international

the poorest can likewise be underrepresented; some are home-

poverty lines of $1.25 and $2.00 a day in 2005 prices, converted

less or nomadic and hard to reach in standard household survey

to local currency using the PPP conversion factors estimated by

designs, and some may be physically or socially isolated and

the International Comparison Program. • Survey year is the year in

thus less likely to be interviewed. This can bias both poverty and

which the underlying data were collected or, when the data collection

inequality measurement if not corrected for (Korinek, Mistiaen,

period bridged two calendar years, the year in which most of the data

and Ravallion 2007).

were collected. • Population below $1.25 a day and population below $2 a day are the percentages of the population living on less

International poverty lines

than $1.25 a day and $2 a day at 2005 international prices. As a

International comparisons of poverty estimates entail both concep-

result of revisions in PPP exchange rates, consumer price indexes,

tual and practical problems. Countries have different definitions of

or welfare aggregates, poverty rates for individual countries cannot

poverty, and consistent comparisons across countries can be diffi-

be compared with poverty rates reported in earlier editions. The

cult. Local poverty lines tend to have higher purchasing power in rich

PovcalNet online database and tool always contain the most recent

countries, where more generous standards are used, than in poor

full time series of comparable country data. • Poverty gap is the

countries. Poverty measures based on an international poverty line

mean shortfall from the poverty line (counting the nonpoor as hav-

attempt to hold the real value of the poverty line constant across

ing zero shortfall), expressed as a percentage of the poverty line.

countries, as is done when making comparisons over time. Since

This measure reflects the depth of poverty as well as its incidence.

World Development Report 1990 the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what

Data sources

poverty means in the world’s poorest countries. The welfare of people

The poverty measures are prepared by the World Bank’s Develop-

living in different countries can be measured on a common scale by

ment Research Group. The international poverty lines are based on

adjusting for differences in the purchasing power of currencies. The

nationally representative primary household surveys conducted by

commonly used $1 a day standard, measured in 1985 international

national statistical offices or by private agencies under the supervi-

prices and adjusted to local currency using purchasing power parities

sion of government or international agencies and obtained from

(PPPs), was chosen for World Development Report 1990 because it

government statistical offices and World Bank Group country depart-

was typical of the poverty lines in low-income countries at the time.

ments. For details on data sources and methods used in deriving

Early editions of World Development Indicators used PPPs from the

the World Bank’s latest estimates, see http://iresearch.worldbank

Penn World Tables to convert values in local currency to equivalent

.org/PovcalNet/index.htm.

purchasing power measured in U.S dollars. Later editions used 1993 consumption PPP estimates produced by the World Bank.

References

International poverty lines were recently revised using the new

Chen, Shaohua, and Martin Ravallion. 2011. “The Developing World Is

data on PPPs compiled in the 2005 round of the International Com-

Poorer Than We Thought, But No Less Successful in the Fight Against

parison Program, along with data from an expanded set of house-

Poverty.” Quarterly Journal of Economics 125(4): 1577–1625.

hold income and expenditure surveys. The new extreme poverty

Korinek, Anton, Johan A. Mistiaen, and Martin Ravallion. 2007. “An

line is set at $1.25 a day in 2005 PPP terms, which represents the

Econometric Method of Correcting for Unit Nonresponse Bias in

mean of the poverty lines found in the poorest 15 countries ranked

Surveys.” Journal of Econometrics 136: 213–35.

by per capita consumption. The new poverty line maintains the same

Ravallion, Martin, Guarav Datt, and Dominique van de Walle. 1991.

standard for extreme poverty—the poverty line typical of the poorest

“Quantifying Absolute Poverty in the Developing World.” Review of

countries in the world—but updates it using the latest information on the cost of living in developing countries. PPP exchange rates are

Income and Wealth 37(4): 345–61. Ravallion, Martin, Shaohua Chen, and Prem Sangraula. 2009. “Dol-

used to estimate global poverty because they take into account the

lar a Day Revisited.” World Bank Economic Review 23(2): 163–84.

local prices of goods and services not traded internationally. But

World Bank. 1990. World Development Report 1990: Poverty. Wash-

PPP rates were designed for comparing aggregates from national accounts, not for making international poverty comparisons. As a result, there is no certainty that an international poverty line measures the same degree of need or deprivation across countries. So-called poverty PPPs, designed to compare the consumption of the poorest people in the world, might provide a better basis for comparison of poverty across countries. Work on these measures

ington, DC. ———. 2000. Global Economic Prospects and the Developing Countries. Washington, DC. ———. 2008. Poverty Data: A Supplement to World Development Indicators 2008. Washington, DC. ———. 2013. Global Monitoring Report: Rural-Urban Dynamics and the Millennium Development Goals. Washington, DC.

is ongoing.

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PEOPLE

34â&#x20AC;&#x192;

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The indicators in the People section present demographic trends and forecasts alongside indicators of education, health, jobs, social protection, poverty, and the distribution of income. Together they provide a multidimensional portrait of human development. Data updates in this edition include provisional estimates of global and regional extreme poverty rates for 2010—measured as the proportion of the population living on less than $1.25 a day. The availability, frequency, and quality of poverty monitoring data remain low, especially in small states and in countries and territories with fragile situations. While estimates may change marginally as additional country data become available, it is now clear that the first Millennium Development Goal target—cutting the global extreme poverty rate to half its 1990 level—was achieved before the 2015 target date. In 1990, the benchmark year for the Millennium Development Goals, the extreme poverty rate was 43.1 percent. Estimates for 2010 show that the extreme poverty rate had fallen to 20.6 percent. In addition to extreme poverty rates, the People section includes many other indicators used to monitor the Millennium Development Goals. Following the adoption of the Millennium Declaration by the United Nations General Assembly in 2000, various international agencies including the World Bank resolved to invest in using highquality harmonized data to monitor the Millennium Development Goals. These efforts range from providing technical and financial assistance to strengthen country statistical systems to fostering international collaboration through participation in the United Nations Inter-Agency and Expert Group on the Millennium Development Goal Indicators and several thematic

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inter-agency groups. These forums bring together agency subject matter specialists with leading academic and local experts. Successful thematic inter-agency efforts have improved estimates on child mortality, child malnutrition, as well as maternal mortality, one of the most challenging indicators to measure. For 2013 the People section includes improved child malnutrition indicators produced by the United Nations Children’s Fund, the World Health Organization, and the World Bank using a harmonized dataset and the same statistical methodology to estimate global, regional, and income group aggregates. Another result is the improvement in monitoring HIV prevalence. Initial efforts relied solely on country surveillance systems that collected data from pregnant women who attended sentinel antenatal clinics. Now the methodology measuring this indicator uses all available ­information— including blood test data collected through nationally representative sample s ­ urveys—and the models account for the effects of anti­retro­ viral therapy and urbanization (see About the data for online table 2.20). In addition to providing insights into differences between countries and between groups of countries, the People section includes indicators disaggregated by location and by socioeconomic and demographic strata within countries, such as gender, age, and wealth quintile. These data provide a deeper perspective on the disparities within countries. New indicators for 2013 include sex-disaggregated data on the under-five mortality rate, sex-disaggregated youth labor force participation rates, and numerous indicators from the World Bank’s Atlas of Social Protection Indicators of Resilience and Equity database.

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World Development Indicators 2013 35


Highlights East Asia & Pacific: Narrowing gaps in access to improved sanitation facilities and water sources During the past 20 years the share of the developing world’s population

Share of population with access (%) To improved sanitation facilities 100

To improved water sources 90

86

75

with access to improved sanitation facilities and water sources has risen substantially. East Asia and Pacific has made above-average

90

progress on improving both and narrowing the gap between them,

71

68

driven by a rapid expansion in access to improved sanitation from

71

66

30 percent in 1990 to 66 percent in 2010. In South Asia progress on

61

56

50

access to an improved water source was almost on par with East Asia

48

and Pacific, but access to improved sanitation facilities remains very

38

37 30

25

26

22

low. Indeed, the region fares only marginally better than Sub-­Saharan

31

Africa, which progressed substantially slower over the past two decades. But both regions made progress on expanding the share of

0

1990 2010 Developing countries

1990 2010 East Asia & Pacific

1990 2010 South Asia

the population with access to an improved water source.

1990 2010 Sub-Saharan Africa

Source: Online table 2.16.

Europe & Central Asia: Variation in prevalence of underweight children Malnourishment in children has been linked to poverty, low levels of

Prevalence of child malnutrition, underweight, most recent year, 2005–11 (% of children under age 5)

education, and poor access to health services. It increases the risk of

25

death, inhibits cognitive development, and can adversely affect health status during adulthood. Adequate nutrition is a cornerstone for devel-

20

opment, health, and the survival of current and future generations. 15

Europe and Central Asia has the lowest prevalence (1.5 percent) of underweight children among developing regions, but there is substan-

10

tial variation across countries. At 3.2 percent, prevalence in Moldova is more like that in Latin America and the Caribbean countries; at

5

5.3 percent, prevalence in Armenia is more like that in East Asia and Sub-Saharan Africa

Middle East & North Africa

East Asia & Pacific

Latin America & Caribbean

Europe & Central Asia

Tajikistan

Albania

Armenia

Moldova

Bulgaria

Pacific countries; and at 6.3 percent, prevalence in Albania is more Georgia

0

like that in Middle East and North African countries. Tajikistan, a lowincome country, at 15 percent has the highest proportion of underweight children in the region.

Source: Online table 2.18.

Latin America & Caribbean: Income inequality falling but remains among the world’s highest T he Gini coefficient, a common indicator of income inequality, mea-

Gini coefficient, most recent value, 2000–11 65

sures how much the per capita income distribution within a country Latin America & Caribbean countries Other upper middle income countries Other countries

55

deviates from perfect equality. (A Gini coefficient of 0 represents

Inequality increased

perfect equality, and a value of 100 perfect inequality.) Trends in developing countries over the past two decades suggest that many

Inequality unchanged

countries have become less unequal, but trends differ by region and by income. Inequality in Latin America and the Caribbean fell notably

45

in almost all upper middle-income countries but remains among the

Inequality decreased

highest w ­ orldwide. The Gini coefficient is higher than the Latin America and the Caribbean average in only two other upper middle-

35

income countries and in only a few low- and lower middle-­income 25 25

countries. 35

45

55

65

Gini coefficient (most recent value, 1990–99) Source: Online table 2.9 and World Bank PovcalNet.

36 

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Middle East & North Africa: Many educated women are not participating in the labor force More than 70 percent of girls in the Middle East and North Africa

Gross secondary enrollment ratio for girls (% of relevant age group) Labor force participation rate for women (% of women ages 15 and older)

attend secondary school (higher than most developing countries), but labor force participation rates have stagnated at around 20 percent

100

since 1990 (lower than most developing countries). Eight of the ten countries with the largest gap between their labor force participation

75

rate and secondary enrollment ratio are in the Middle East and North Africa. (Costa Rica and Sri Lanka are the only countries in the top 10

50

not from the region.) Morocco and the Republic of Yemen have the smallest gap, but they also have the lowest secondary enrollment ratio.

25

Middle East & North Africa, 2011 Developing countries, 2011

Yemen, Rep., 2011

Morocco, 2007

Egypt, Arab Rep., 2010

Iran, Islamic Rep., 2011

online table 2.4).

Lebanon, 2011

has the largest gender gap in the share of vulnerable employment (see

Jordan, 2010

0

Algeria, 2009

and in what condition women work are also important. The region also

Tunisia, 2009

In addition to whether women participate in economic activities, where

Source: Online tables 2.2 and 2.11.

South Asia: In India more girls than boys die before their fifth birthday The ratio of girls’ to boys’ under-five mortality rate varies across developing countries, but on average it is 0.96. East Asia and Pacific

Ratio of girls’ to boys’ under-five mortality rate, 2011 Lowest in region

has the largest variation among developing regions. At one extreme

East Asia Palau 0.59 & Pacific

is Palau, where mortality rates for boys are two-thirds higher than that for girls. Biologically, boys are more vulnerable than girls, so under-five mortality rates are usually higher for boys than girls. This biological advantage can be reversed, however, by socioeconomic factors such as gender inequalities in nutrition and medical care or discrimination against girls. India and the Solomon Islands are the

Europe & Central Asia

0.92 Azerbaijan

Jamaica 0.75

Middle East & North Africa

0.93 Grenada

Tunisia 0.85

South Asia

fifth birthday. In India under-five mortality rates for girls and boys have at around 1.1 since 1990.

1.06 Solomon Islands

Belarus 0.73

Latin America & Caribbean

only developing countries where more girls than boys die before their improved, but the ratio of girls’ to boys’ under-five deaths has stayed

Highest in region

0.96 Iran, Islamic Rep.

Sri Lanka 0.81

Sub-Saharan Africa

1.09 India

Eritrea 0.83

0.50

0.98 South Sudan

0.75

1.00 (parity)

1.25

Source: Online table 2.21.

Sub-­Saharan Africa: Different demographic transition paths at varying speeds Today, one of every nine children in Sub-­S aharan Africa dies before their fifth birthday, and fertility rates there remain higher than any-

Under-five mortality rate (per 1,000 live births) 400

Côte d’Ivoire Gabon Mali Niger Sub-Saharan Africa

where else in the developing world. Sub-­S aharan countries progressed over the past four decades, but they are moving along different paths at varying speeds. Gabon, ahead of the curve, had the

1970 1970 1990

300

under-five mortality and total fertility rates in 1980 that the region has today. Niger made little progress between 1970 and 1990 but

1970

200

has since seen rapid improvements in its under-five mortality rate

1980

and a marginal decline in its fertility rate. In 1970 Côte d’Ivoire had an average mortality rate but a high fertility rate. Its mortality rate

2010

100

declined quickly until 1980, thereafter its fertility rate declined rapidly and overtook the region by 2010. Mali, which had the highest under-five mortality rate in 1970, took until 2010 to reach the rate

1970

2010

2010

1980

2010

2010

0 3

4

5

6

7

8

9

Total fertility rate (births per woman)

the region passed in 1990.

Source: Online table 2.21.

Economy

States and markets

Global links

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World Development Indicators 2013 37


2 People Prevalence Under-five of child mortality malnutrition, rate underweight

estimate 1,000 % of per 100,000 women ages population % of relevant live births 15–19 ages 15–49 age group

% of children under age 5

per 1,000 live births

2005–11 a

2011

2010

2011

2011

2011

Afghanistan

% ages 15–24

% ages 15 and older

workers % of total employment

% of total labor force

% of total

2005–11a

2011

2007–11 a

2007–11 a

2007–11 a

..

101

460

103

<0.1

..

..

49

..

..

..

Albania

6.3

14

27

16

..

..

99

60

..

13.8

..

Algeria

3.7

30

97

6

..

94

92

44

..

10.0

..

American Samoa

..

..

..

..

..

..

..

..

..

..

..

Andorra

..

3

..

..

..

63

..

..

..

..

..

15.6

158

450

153

2.1

..

73

70

..

..

..

..

8

..

..

..

98

..

..

..

..

..

Argentina

2.3

14

77

55

0.4

..

99

61

19

7.2

..

Armenia

5.3

18

30

34

0.2

83

100

59

38

28.6

22 40

Angola Antigua and Barbuda

Aruba

..

..

..

28

..

..

99

..

4

5.7

Australia

..

5

7

13

0.2

..

..

66

9

5.1

37

Austria

..

4

4

10

0.4

..

..

61

9

4.1

27

8.4

45

43

32

<0.1

93

100

65

55

5.4

7

..

16

47

29

2.8

..

..

74

..

13.7

52 ..

Azerbaijan Bahamas, The Bahrain

..

10

20

15

..

..

100

71

2

..

41.3

46

240

70

<0.1

..

77

71

..

5.0

..

..

20

51

41

0.9

111

..

70

..

11.2

47

Belarus

1.3

6

4

21

0.4

104

100

56

..

..

..

Belgium

..

4

8

12

0.3

..

..

54

10

7.1

30

Bangladesh Barbados

Belize

4.9

17

53

72

2.3

110

..

65

..

8.2

..

Benin

20.2

106

350

100

1.2

75

55

73

..

..

..

..

..

..

..

..

..

..

..

..

..

..

Bhutan

12.7

54

180

46

0.3

74

72

71

3.1

27

Bolivia

4.5

51

190

75

0.3

..

99

72

55

3.4

29

Bosnia and Herzegovina

1.6

8

8

14

..

76

100

46

25

27.6

..

11.2

26

160

45

23.4

..

95

77

..

..

..

2.2

16

56

76

0.3

..

98

70

25

8.3

36

Bermuda

Botswana Brazil

103b

Brunei Darussalam

..

7

24

23

..

120

100

66

..

..

..

Bulgaria

..

12

11

38

<0.1

..

98

54

9

11.2

37 ..

Burkina Faso

26.0

146

300

119

1.1

..

39

84

..

3.3

Burundi

35.2

139

800

20

1.3

62

78

83

..

..

..

Cambodia

29.0

43

250

35

0.6

90

87

83

69

0.2

21

Cameroon

16.6

127

690

118

4.6

78

83

71

76

3.8

..

Canada

..

6

12

12

0.3

..

..

67

..

7.4

36

Cape Verde

..

21

79

72

1.0

95

98

67

..

..

..

Cayman Islands

..

..

..

..

..

..

99

..

..

4.0

44

Central African Republic

28.0

164

890

100

4.6

43

65

79

..

..

..

Chad

..

169

1,100

143

3.1

38

47

72

..

..

..

Channel Islands

..

..

..

9

..

..

..

..

..

..

..

Chile

0.5

9

25

56

0.5

..

99

60

24

7.1

..

China

3.4

15

37

9

<0.1

..

99

74

..

4.1

..

..

..

..

4

..

91

..

59

7

3.4

32

Hong Kong SAR, China Macao SAR, China

38 

Maternal Adolescent Prevalence Primary Youth Labor force Vulnerable Unemployment Female mortality fertility of HIV completion literacy participation employment legislators, ratio rate rate rate rate senior Unpaid family officials, and workers and managers Modeled births per own-account

..

..

..

4

..

..

100

72

4

2.6

31

Colombia

3.4

18

92

69

0.5

112

98

67

49

11.6

..

Comoros

..

79

280

52

<0.1

..

86

58

..

..

..

Congo, Dem. Rep.

28.2

168

540

177

..

..

65

71

..

..

..

Congo, Rep.

11.8

99

560

114

3.3

..

80

71

..

..

..

Front

?

World Development Indicators 2013

User guide

World view

People Environment


People 2 Prevalence Under-five of child mortality malnutrition, rate underweight

Costa Rica Côte d’Ivoire Croatia Cuba

% of children under age 5

per 1,000 live births

2005–11 a

2011

Maternal Adolescent Prevalence Primary Youth Labor force Vulnerable Unemployment Female mortality fertility of HIV completion literacy participation employment legislators, ratio rate rate rate rate senior Unpaid family officials, and workers and managers Modeled births per own-account

estimate 1,000 % of per 100,000 women ages population % of relevant live births 15–19 ages 15–49 age group 2010

2011

2011

2011

% ages 15–24

% ages 15 and older

workers % of total employment

% of total labor force

% of total

2005–11a

2011

2007–11 a

2007–11 a

2007–11 a

1.1

10

40

63

0.3

99

98

63

20

7.7

29.4

115

400

110

3.0

59

67

67

..

..

35 ..

..

5

17

13

<0.1

..

100

53

18

13.4

25 ..

1.3

6

73

44

0.2

99

100

57

..

2.5

Curaçao

..

..

..

..

..

..

..

..

..

..

..

Cyprus

..

3

10

6

..

..

100

65

14

7.7

14

Czech Republic

..

4

5

10

<0.1

..

..

59

14

6.7

26

Denmark

..

4

12

5

0.2

..

..

64

6

7.6

28

29.6

90

200

20

1.4

57b

..

52

..

..

..

..

12

..

..

..

94

..

..

..

..

..

3.4

25

150

105

0.7

92

97

65

37

12.4

31

Djibouti Dominica Dominican Republic Ecuador

..

23

110

81

0.4

112

99

68

42

4.2

..

Egypt, Arab Rep.

6.8

21

66

42

<0.1

..

88

49

27

9.0

11

El Salvador

6.6

15

81

77

0.6

101

96

62

38

7.0

29

Equatorial Guinea

..

118

240

116

4.7

52

98

87

..

..

..

Eritrea

..

68

240

56

0.6

..

89

85

..

..

..

Estonia

..

4

2

18

1.3

..

100

62

5

12.5

36

Ethiopia

29.2

77

350

53

1.4

58

55

84

..

..

..

Faeroe Islands

..

..

..

..

..

..

..

..

..

..

..

Fiji

..

16

26

43

<0.1

103

..

60

..

8.6

..

Finland

..

3

5

9

<0.1

..

..

60

9

7.7

32

France

..

4

8

6

0.4

..

..

56

7

9.3

39

French Polynesia

..

..

..

49

..

..

..

58

..

11.7

..

Gabon

..

66

230

83

5.0

..

98

61

..

..

..

15.8

101

360

69

1.5

66

67

78

..

..

..

1.1

21

67

41

0.2

..

100

64

63

15.1

34 30

Gambia, The Georgia Germany

1.1

4

7

7

0.2

..

..

60

7

5.9

Ghana

14.3

78

350

64

1.5

99 b

81

69

..

..

..

Greece

1.1

4

3

10

0.2

..

99

55

29

17.7

23

Greenland

..

..

..

..

..

..

..

..

..

..

..

Grenada

..

13

24

37

..

..

..

..

..

..

..

Guam

..

..

..

50

..

..

..

61

..

..

..

Guatemala

13.0

30

120

103

0.8

..

87

68

..

4.1

..

Guinea

20.8

126

610

138

1.4

..

63

72

..

..

..

Guinea-Bissau

17.2

161

790

99

2.5

..

72

73

..

..

..

Guyana

11.1

36

280

57

1.1

85

..

60

..

21.0

..

Haiti

18.9

70

350

42

1.8

..

72

65

..

..

..

8.6

21

100

87

..

101

95

62

53

4.8

..

..

6

21

14

<0.1

..

99

51

7

10.9

40 40

Honduras Hungary Iceland

..

3

5

12

0.3

..

..

75

8

7.1

India

43.5

61

200

77

..

..

81

56

81

3.5

14

Indonesia

18.6

32

220

43

0.3

..

99

68

57

6.6

22 13

Iran, Islamic Rep.

..

25

21

26

0.2

106

99

45

42

10.5

7.1

38

63

88

..

..

83

42

..

..

..

Ireland

..

4

6

11

0.3

..

..

61

12

14.4

33

Isle of Man

..

..

..

..

..

..

..

..

..

..

..

Israel

..

4

7

14

0.2

..

..

57

7

5.6

32

Iraq

Economy

States and markets

Global links

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World Development Indicators 2013 39


2 People Prevalence Under-five of child mortality malnutrition, rate underweight % of children under age 5

per 1,000 live births

2005–11 a

2011

Italy

estimate 1,000 % of per 100,000 women ages population % of relevant live births 15–19 ages 15–49 age group 2010

2011

2011

2011

% ages 15–24

% ages 15 and older

workers % of total employment

% of total labor force

% of total

2005–11a

2011

2007–11 a

2007–11 a

2007–11 a

..

4

4

5

0.4

..

100

48

18

8.4

25

1.9

18

110

71

1.8

..

95

64

37

12.7

..

..

3

5

6

<0.1

..

..

60

11

4.5

..

Jordan

1.9

21

63

24

..

..

99

42

9

12.9

..

Kazakhstan

4.9

28

51

26

0.2

100

72

30

5.4

38

16.4

73

360

99

6.2

..

93

67

..

..

..

..

47

..

..

..

..

..

..

..

..

..

18.8

33

81

1

..

..

100

78

..

..

..

..

5

16

5

<0.1

..

..

60

25

3.4

10

Jamaica Japan

Kenya Kiribati Korea, Dem. Rep. Korea, Rep.

108b

Kosovo

..

..

..

..

..

..

..

..

..

45.4

..

Kuwait

1.7

11

14

14

..

..

99

68

..

..

..

Kyrgyz Republic

2.7

31

71

33

0.4

96

100

67

..

8.2

..

31.6

42

470

32

0.3

93

84

78

..

..

..

..

8

34

14

0.7

93

100

61

8

15.4

45

Lao PDR Latvia Lebanon

5.2

9

25

16

<0.1

87

99

46

28

9.0

8

Lesotho

13.5

86

620

63

23.3

68

92

66

..

25.3

..

Liberia

20.4

78

770

127

1.0

66

77

61

79

3.7

..

5.6

16

58

3

..

..

100

53

..

..

..

Liechtenstein

..

2

..

..

..

101

..

..

..

..

..

Lithuania

..

6

8

17

<0.1

95

100

59

8

15.4

38

..

3

20

9

0.3

..

..

57

6

4.9

24

1.8

10

10

19

..

..

99

56

23

31.4

28 ..

Libya

Luxembourg Macedonia, FYR Madagascar

..

62

240

125

0.3

73

65

86

..

..

Malawi

13.8

83

460

108

10.0

71

87

83

..

..

..

Malaysia

12.9

7

29

11

0.4

..

98

60

22

3.4

25 ..

Maldives

17.8

11

60

11

<0.1

107

99

66

..

..

Mali

27.9

176

540

172

1.1

55

44

53

83

..

..

..

6

8

13

<0.1

..

98

51

9

6.4

23 ..

Malta Marshall Islands Mauritania Mauritius Mexico Micronesia, Fed. Sts. Moldova Monaco

..

26

..

..

..

97

..

..

..

..

15.9

112

510

73

1.1

..

68

54

..

31.2

..

..

15

60

33

1.0

..

97

60

15

7.9

23 31

3.4

16

50

67

0.3

104

98

62

29

5.3

..

42

100

20

..

..

..

..

..

..

..

3.2

16

41

30

0.5

91

99

42

29

6.7

38

..

4

..

..

..

..

..

..

..

..

..

Mongolia

5.3

31

63

19

<0.1

115

96

60

58

..

47

Montenegro

2.2

7

8

15

..

99 b

99

..

..

19.7

31

Morocco

3.1

33

100

12

0.2

99 b

79

50

52

8.9

13

Mozambique

18.3

103

490

129

11.3

56

72

85

..

..

..

Myanmar

22.6

62

200

13

0.6

..

96

79

..

..

..

Namibia

17.5

42

200

58

13.4

..

93

64

14

37.6

..

Nepal

29.1

48

170

90

0.3

..

83

84

..

2.7

..

Netherlands

..

4

6

4

0.2

..

..

65

11

4.4

30

New Caledonia

..

..

..

20

..

..

100

58

..

..

..

New Zealand

..

6

15

21

<0.1

..

..

68

12

6.5

40

Nicaragua Niger

40 

Maternal Adolescent Prevalence Primary Youth Labor force Vulnerable Unemployment Female mortality fertility of HIV completion literacy participation employment legislators, ratio rate rate rate rate senior Unpaid family officials, and workers and managers Modeled births per own-account

World Development Indicators 2013

5.7

26

95

106

0.2

..

87

63

47

8.0

..

39.9

125

590

196

0.8

46

37

65

..

..

..

Front

?

User guide

World view People view People Environment Environment


People 2 Prevalence Under-five of child mortality malnutrition, rate underweight

Nigeria

Maternal Adolescent Prevalence Primary Youth Labor force Vulnerable Unemployment Female mortality fertility of HIV completion literacy participation employment legislators, ratio rate rate rate rate senior Unpaid family officials, and workers and managers Modeled births per own-account

estimate 1,000 % of per 100,000 women ages population % of relevant live births 15–19 ages 15–49 age group

% of children under age 5

per 1,000 live births

2005–11 a

2011

2010

2011

2011

2011

% ages 15–24

% ages 15 and older

workers % of total employment

% of total labor force

% of total

2005–11a

2011

2007–11 a

2007–11 a

2007–11 a

26.7

124

630

113

3.7

..

72

56

..

..

Northern Mariana Islands

..

..

..

..

..

..

..

..

..

..

..

Norway

..

3

7

8

0.2

..

..

66

5

3.3

31

Oman Pakistan Palau Panama Papua New Guinea Paraguay Peru

..

8.6

9

32

9

..

107

98

61

..

..

..

30.9

72

260

29

<0.1

67

71

53

63

5.0

3

..

19

..

..

..

..

..

..

..

..

..

3.9

20

92

77

0.8

101

98

66

29

4.5

46

18.1

58

230

63

0.7

..

68

72

..

4.0

..

3.4

22

99

68

0.3

..

99

72

42

5.6

34

4.5

18

67

50

0.4

97

97

76

40

7.8

19

20.7

25

99

48

<0.1

..

98

64

41

7.0

55

Poland

..

6

5

13

<0.1

..

100

56

18

9.6

38

Portugal

..

3

8

13

0.7

..

100

62

16

12.7

33

Puerto Rico

..

..

20

51

..

..

87

45

..

15.7

43

Qatar

..

8

7

16

..

96

97

86

0

0.6

10

Romania

..

13

27

29

<0.1

..

97

56

32

7.4

31

Russian Federation

..

12

34

25

..

..

100

63

6

6.6

37

11.7

54

340

36

2.9

..

77

86

..

2.4

..

Samoa

..

19

100

26

..

98

99

61

..

..

..

San Marino

..

2

..

..

..

93

..

..

..

2.6

18

14.4

89

70

58

1.0

115

95

60

..

..

..

5.3

9

24

20

..

106

98

50

..

5.4

8

19.2

65

370

93

0.7

63

65

77

..

..

..

1.8

7

12

20

<0.1

99

99

..

27

19.2

33 ..

Philippines

Rwanda

São Tomé and Príncipe Saudi Arabia Senegal Serbia Seychelles

..

14

..

..

..

125

99

..

..

..

21.3

185

890

112

1.6

74

59

68

..

..

..

Singapore

..

3

3

6

<0.1

..

100

67

10

2.9

34

Sint Maarten

..

..

..

..

..

..

..

..

..

..

..

Slovak Republic

..

8

6

17

<0.1

..

..

59

12

13.5

31

Sierra Leone

Slovenia

..

3

12

5

<0.1

..

100

59

13

8.2

38

Solomon Islands

11.5

22

93

66

..

..

..

67

..

..

..

Somalia

32.8

180

1,000

68

0.7

..

..

57

..

..

..

8.7

47

300

52

17.3

..

98

52

10

24.7

31

South Sudan

..

121

..

..

3.1

..

..

..

..

..

..

Spain

..

4

6

11

0.4

..

100

59

11

21.6

30

South Africa

Sri Lanka

21.6

12

35

22

<0.1

..

98

55

42

4.9

24

St. Kitts and Nevis

..

7

..

..

..

93

..

..

..

..

..

St. Lucia

..

16

35

57

..

93

..

71

..

14.0

..

St. Martin

..

..

..

..

..

..

..

..

..

..

..

St. Vincent and Grenadines

..

21

48

55

..

..

..

67

..

..

..

86d

730

55

0.4 d

..

87

54

..

..

.. ..

Sudan

31.7

Suriname

7.5

30

130

36

1.0

88

98

55

..

..

Swaziland

7.3

104

320

71

26.0

77

94

57

..

..

..

..

3

4

6

0.2

..

..

64

7

7.5

35

Sweden Switzerland

..

4

8

4

0.4

..

..

68

9

4.1

33

Syrian Arab Republic

10.1

15

70

38

..

106

95

43

33

8.4

9

Tajikistan

15.0

63

65

26

0.3

104

100

66

..

..

..

Economy

States and markets

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World Development Indicators 2013 41


2 People Prevalence Under-five of child mortality malnutrition, rate underweight

Maternal Adolescent Prevalence Primary Youth Labor force Vulnerable Unemployment Female mortality fertility of HIV completion literacy participation employment legislators, ratio rate rate rate rate senior Unpaid family officials, and workers and managers Modeled births per own-account

estimate 1,000 % of per 100,000 women ages population % of relevant live births 15–19 ages 15–49 age group

% ages 15–24

% ages 15 and older

workers % of total employment

% of total labor force

% of total

2005–11a

2011

2007–11 a

2007–11 a

2007–11 a

% of children under age 5

per 1,000 live births

2005–11 a

2011

2010

2011

2011

Tanzania

16.2

68

460

129

5.8

81b

77

89

..

..

..

Thailand

7.0

12

48

38

1.2

..

98

72

54

0.7

25

2011

Timor-Leste

45.3

54

300

55

..

72

80

57

70

3.6

..

Togo

20.5

110

300

57

3.4

77

82

81

..

..

..

Tonga

..

15

110

19

..

..

99

64

..

..

..

Trinidad and Tobago

..

28

46

32

1.5

..

100

66

..

4.6

..

3.3

16

56

5

<0.1

..

97

48

..

13.0

..

Turkey

..

15

20

32

<0.1

..

98

50

33

9.8

10

Turkmenistan

..

53

67

17

..

..

100

61

..

..

..

Turks and Caicos Islands

..

..

..

..

..

..

..

..

..

5.4

..

Tuvalu

1.6

30

..

..

..

..

..

..

..

..

..

Uganda

16.4

90

310

131

7.2

55

87

78

..

4.2

..

Ukraine

..

10

32

27

0.8

97

100

59

..

7.9

39

United Arab Emirates

..

7

12

24

..

..

95

79

1

4.0

10

United Kingdom

..

5

12

30

0.3

..

..

62

12

7.8

34

United States

..

8

21

30

0.7

..

..

64

..

8.9

43

Uruguay

..

10

29

59

0.6

..

99

66

22

6.0

40

4.4

49

28

13

..

93

100

61

..

..

..

11.7

13

110

51

..

..

94

71

70

4.6

29

Tunisia

Uzbekistan Vanuatu Venezuela, RB Vietnam Virgin Islands (U.S.) West Bank and Gaza Yemen, Rep.

3.7

15

92

88

0.6

95

99

66

33

8.3

..

20.2

22

59

24

0.5

104

97

77

63

2.0

..

..

..

..

23

..

..

..

62

..

..

..

2.2

22

..

49

..

91

99

41

26

23.7

10

..

77

200

69

0.2

63

85

49

..

14.6

..

Zambia

14.9

83

440

140

12.5

..

74

79

..

..

..

Zimbabwe

10.1

67

570

56

14.9

..

99

86

..

..

..

15.7 w

51 w

210 w

53 w

0.8 w

90 w

90 w

64 w

.. w

World

5.9 w

Low income

22.6c

95

410

92

2.4

68

74

75

..

..

Middle income

16.0 c

46

190

50

..

..

91

64

..

5.2

Lower middle income

24.3c

62

260

66

..

..

84

58

71

4.9

Upper middle income

2.9c

20

62

30

0.6

..

99

69

..

5.0

Low & middle income

17.4 c

56

230

57

1.0

89

88

65

..

5.2

East Asia & Pacific

5.5c

21

83

19

0.2

..

99

73

..

4.2

Europe & Central Asia

1.5c

21

32

26

..

98

99

59

18

8.0

Latin America & Carib.

3.1c

19

81

71

0.4

102

97

66

31

7.7

Middle East & N. Africa

6.3c

32

81

37

..

90

91

46

..

10.6 3.5

South Asia

33.2c

62

220

71

..

..

79

57

78

Sub-­Saharan Africa

21.4 c

109

500

106

4.9

70

72

70

..

..

High income

1.7c

6

14

17

0.4

100

100

60

..

8.1

Euro area

..

4

6

8

0.3

99

100

57

11

10.1

a. Data are for the most recent year available. b. Data are for 2012. c. Calculated by World Bank staff using the United Nations Children’s Fund, World Health Organization, and World Bank harmonized database and aggregation method. d. Excludes South Sudan.

42 

World Development Indicators 2013

Front

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User guide

World view

People Environment


People 2 About the data Though not included in the table due to space limitations, many

WHO, the United Nations Population Division, the World Bank,

indicators in this section are available disaggregated by sex, place

and other universities and research institutes, has developed and

of residence, wealth, and age in the World Development Indicators

adopted a statistical method that uses all available information to

database.

reconcile differences. Trend lines are obtained by fitting a countryspecific regression model of mortality rates against their reference

Child malnutrition

dates. (For further discussion of childhood mortality estimates,

Good nutrition is the cornerstone for survival, health, and devel-

see UN Inter-agency Group for Child Mortality Estimation 2012; for

opment. Well-nourished children perform better in school, grow

detailed background data and for a graphic presentation, see www

into healthy adults, and in turn give their children a better start

.childmortality.org).

in life. Well-nourished women face fewer risks during pregnancy and childbirth, and their children set off on firmer developmental

Maternal mortality

paths, both physically and mentally. Undernourished children have

Measurements of maternal mortality are subject to many types

lower resistance to infection and are more likely to die from com-

of errors. In countries with incomplete vital registration systems,

mon childhood ailments such as diarrheal diseases and respiratory

deaths of women of reproductive age or their pregnancy status may

infections. Frequent illness saps the nutritional status of those who

not be reported, or the cause of death may not be known. Even in

survive, locking them into a vicious cycle of recurring sickness and

high-income countries with reliable vital registration systems, mis-

faltering growth.

classification of maternal deaths has been found to lead to serious

The proportion of underweight children is the most common child

underestimation. Surveys and censuses can be used to measure

malnutrition indicator. Being even mildly underweight increases the

maternal mortality by asking respondents about survivorship of sis-

risk of death and inhibits cognitive development in children. And

ters. But these estimates are retrospective, referring to a period

it perpetuates the problem across generations, as malnourished

approximately five years before the survey, and may be affected by

women are more likely to have low-birthweight babies. Estimates

recall error. Further, they reflect pregnancy-related deaths (deaths

of prevalence of underweight children are from the World Health

while pregnant or within 42 days of pregnancy termination, irrespec-

Organization’s (WHO) Global Database on Child Growth and Malnu-

tive of the cause of death) and need to be adjusted to conform to

trition, a standardized compilation of child growth and malnutrition

the strict definition of maternal death.

data from national nutritional surveys. To better monitor global child

Maternal mortality ratios in the table are modeled estimates

malnutrition, the United Nations Children’s Fund (UNICEF), the WHO,

based on work by the WHO, UNICEF, United Nations Population Fund

and the World Bank have jointly produced estimates for 2011 and

(UNFPA), and World Bank and include country-level time series data.

trends since 1990 for regions, income groups, and the world, using

For countries without complete registration data but with other types

a harmonized database and aggregation method.

of data and for countries with no data, maternal mortality is estimated with a multilevel regression model using available national

Under-five mortality

maternal mortality data and socioeconomic information, including

Mortality rates for children and others are important indicators

fertility, birth attendants, and gross domestic product. The meth-

of health status. When data on the incidence and prevalence of

odology differs from that used for previous estimates, so data pre-

diseases are unavailable, mortality rates may be used to identify

sented here should not be compared across editions.

vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across

Adolescent fertility

countries.

Reproductive health is a state of physical and mental well-being

The main sources of mortality data are vital registration sys-

in relation to the reproductive system and its functions and pro-

tems and direct or indirect estimates based on sample surveys

cesses. Means of achieving reproductive health include education

or censuses. A complete vital registration s ­ ystem—covering at

and services during pregnancy and childbirth, safe and effective

least 90 percent of vital events in the population—is the best

contraception, and prevention and treatment of sexually transmitted

source of age-specific mortality data. But complete vital registra-

diseases. Complications of pregnancy and childbirth are the leading

tion systems are fairly uncommon in developing countries. Thus

cause of death and disability among women of reproductive age in

estimates must be obtained from sample surveys or derived by

developing countries.

applying indirect estimation techniques to registration, census,

Adolescent pregnancies are high risk for both mother and child.

or survey data (see Primary data documentation). Survey data are

They are more likely to result in premature delivery, low birthweight,

subject to recall error.

delivery complications, and death. Many adolescent pregnancies are

To make estimates comparable and to ensure consistency across

unintended, but young girls may continue their pregnancies, giving

estimates by different agencies, the United Nations Inter-agency

up opportunities for education and employment, or seek unsafe

Group for Child Mortality Estimation, which comprises UNICEF,

abortions. Estimates of adolescent fertility rates are based on vital

Economy

States and markets

Global links

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World Development Indicators 2013 43


2 People registration systems or, in their absence, censuses or sample sur-

final year of primary education. There are many reasons why the pri-

veys and are generally considered reliable measures of fertility in the

mary completion rate may exceed 100 percent. The numerator may

recent past. Where no empirical information on age-specific fertility

include late entrants and overage children who have repeated one

rates is available, a model is used to estimate the share of births to

or more grades of primary education as well as children who entered

adolescents. For countries without vital registration systems fertility

school early, while the denominator is the number of children at the

rates are generally based on extrapolations from trends observed

entrance age for the last grade of primary education.

in censuses or surveys from earlier years.

Youth literacy Prevalence of HIV

The youth literacy rate for ages 15–24 is a standard measure of

HIV prevalence rates reflect the rate of HIV infection in each coun-

recent progress in student achievement. It reflects the accumulated

try’s population. Low national prevalence rates can be misleading,

outcomes of primary education by indicating the proportion of the

however. They often disguise epidemics that are initially concen-

population that has acquired basic literacy and numeracy skills over

trated in certain localities or population groups and threaten to spill

the previous 10 years or so. In practice, however, literacy is difficult

over into the wider population. In many developing countries most

to measure. Estimating literacy rates requires census or survey mea-

new infections occur in young adults, with young women especially

surements under controlled conditions. Many countries estimate

vulnerable.

the number of literate people from self-reported data. Some use

Data on HIV prevalence are from the Joint United Nations Pro-

educational attainment data as a proxy but apply different lengths

gramme on HIV/AIDS. Changes in procedures and assumptions for

of school attendance or levels of completion. Because definitions

estimating the data and better coordination with countries have

and methods of data collection differ across countries, data should

resulted in improved estimates. New models track the course of

be used cautiously. Generally, literacy encompasses numeracy, the

HIV epidemics and their impacts, making full use of information

ability to make simple arithmetic calculations.

on HIV prevalence trends from surveillance data as well as survey

Data on youth literacy are compiled by the United Nations Educa-

data. The models include the effect of antiretroviral therapy, take

tional, Scientific and Cultural Organization (UNESCO) Institute for

into account reduced infectivity among people receiving antiretrovi-

Statistics based on national censuses and household surveys dur-

ral therapy (which is having a larger impact on HIV prevalence and

ing 1985–2011 and, for countries without recent literacy data, using

allowing HIV-positive people to live longer), and allow for changes in

the Global Age-Specific Literacy Projection Model.

urbanization over time (important because prevalence is higher in urban areas and because many countries have seen rapid urbaniza-

Labor force participation

tion over the past two decades). The estimates include plausible

The labor force is the supply of labor available for producing goods

bounds, available at http://data.worldbank.org, which reflect the

and services in an economy. It includes people who are currently

certainty associated with each of the estimates.

employed, people who are unemployed but seeking work, and firsttime job-seekers. Not everyone who works is included, however.

44 

Primary completion

Unpaid workers, family workers, and students are often omitted,

Many governments publish statistics that indicate how their educa-

and some countries do not count members of the armed forces.

tion systems are working and developing—statistics on enrollment

Labor force size tends to vary during the year as seasonal workers

and efficiency indicators such as repetition rates, pupil–teacher

enter and leave.

ratios, and cohort progression. The primary completion rate, also

Data on the labor force are compiled by the International Labour

called the gross intake ratio to last grade of primary education, is

Organization (ILO) from labor force surveys, censuses, and estab-

a core indicator of an education system’s performance. It reflects

lishment censuses and surveys and from administrative records

an education system’s coverage and the educational attainment

such as employment exchange registers and unemployment insur-

of students. It is a key measure of progress toward the Millennium

ance schemes. Labor force surveys are the most comprehensive

Development Goals and the Education for All initiative. However, a

source for internationally comparable labor force data. Labor force

high primary completion rate does not necessarily mean high levels

data from population censuses are often based on a limited number

of student learning.

of questions on the economic characteristics of individuals, with

The indicator reflects the primary cycle as defined by the Interna-

little scope to probe. Establishment censuses and surveys provide

tional Standard Classification of Education (ISCED97), ranging from

data on the employed population only, not unemployed workers,

three or four years of primary education (in a very small number of

workers in small establishments, or workers in the informal sector

countries) to five or six years (in most countries) and seven (in a

(ILO, Key Indicators of the Labour Market 2001–2002).

small number of countries). It is a proxy that should be taken as an

Besides the data sources, there are other important factors

upper estimate of the actual primary completion rate, since data

that affect data comparability, such as census or survey reference

limitations preclude adjusting for students who drop out during the

period, definition of working age, and geographic coverage. For

World Development Indicators 2013

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People Environment


People 2 country-level information on source, reference period, or definition,

jobs. Such unemployment, often called frictional unemployment,

consult the footnotes in the World Development Indicators data-

results from the normal operation of labor markets.

base or the ILO’s Key Indicators of the Labour Market, 7th edition, database.

Changes in unemployment over time may reflect changes in the demand for and supply of labor, but they may also reflect changes

The labor force participation rates in the table are estimates

in reporting practices. In countries without unemployment or welfare

from the ILO’s Key Indicators of the Labour Market, 7th edition,

benefits people eke out a living in vulnerable employment. In coun-

database. These harmonized estimates use strict data selection

tries with well-developed safety nets workers can afford to wait for

criteria and enhanced methods to ensure comparability across

suitable or desirable jobs. But high and sustained unemployment

countries and over time to avoid the inconsistencies mentioned

indicates serious inefficiencies in resource allocation.

above. Estimates are based mainly on labor force surveys, with

The criteria for people considered to be seeking work, and the

other sources (population censuses and nationally reported esti-

treatment of people temporarily laid off or seeking work for the

mates) used only when no survey data are available. Because other

first time, vary across countries. In many developing countries it

employment data are mostly national estimates, caution should

is especially difficult to measure employment and unemployment

be used when comparing labor force participation rate and other

in agriculture. The timing of a survey can maximize the effects of

employment data.

seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.

Vulnerable employment

Data on unemployment are drawn from labor force sample surveys

The proportion of unpaid family workers and own-account workers in

and general household sample surveys, censuses, and official esti-

total employment is derived from information on status in employ-

mates. Administrative records, such as social insurance statistics

ment. Each group faces different economic risks, and unpaid family

and employment office statistics, are not included because of their

workers and own-account workers are the most vulnerable—and

limitations in coverage.

therefore the most likely to fall into poverty. They are the least likely

Women tend to be excluded from the unemployment count for

to have formal work arrangements, are the least likely to have social

various reasons. Women suffer more from discrimination and from

protection and safety nets to guard against economic shocks, and

structural, social, and cultural barriers that impede them from seek-

are often incapable of generating enough savings to offset these

ing work. Also, women are often responsible for the care of children

shocks. A high proportion of unpaid family workers in a country

and the elderly and for household affairs. They may not be available

indicates weak development, little job growth, and often a large

for work during the short reference period, as they need to make

rural economy.

arrangements before starting work. Further, women are considered

Data on vulnerable employment are drawn from labor force and

to be employed when they are working part-time or in temporary

general household sample surveys, censuses, and official esti-

jobs, despite the instability of these jobs or their active search for

mates. Besides the limitation mentioned for calculating labor force

more secure employment.

participation rates, there are other reasons to limit comparability. For example, information provided by the Organisation for Economic

Female legislators, senior officials, and managers

Co-operation and Development relates only to civilian employment,

Despite much progress in recent decades, gender inequalities

which can result in an underestimation of “employees” and “work-

remain pervasive in many dimensions of life. But while gender

ers not classified by status,” especially in countries with large

inequalities exist throughout the world, they are most prevalent in

armed forces. While the categories of unpaid family workers and

developing countries. Inequalities in the allocation of education,

own-account workers would not be affected, their relative shares

health care, nutrition, and political voice matter because of their

would be.

strong association with well-being, productivity, and economic growth. These patterns of inequality begin at an early age, with boys

Unemployment

routinely receiving a larger share of education and health spending

The ILO defines the unemployed as members of the economically

than girls, for example. The share of women in high-skilled occu-

active population who are without work but available for and seek-

pations such as legislators, senior officials, and managers indi-

ing work, including people who have lost their jobs or who have

cates women’s status and role in the labor force and society at

voluntarily left work. Some unemployment is unavoidable. At any

large. Women are vastly underrepresented in decisionmaking posi-

time some workers are temporarily unemployed—between jobs as

tions in government, although there is some evidence of recent

employers look for the right workers and workers search for better

improvement.

Economy

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World Development Indicators 2013 45


2 People Definitions

Data sources

• Prevalence of child malnutrition, underweight, is the percent-

Data on child malnutrition prevalence are from the WHO’s Global

age of children under age 5 whose weight for age is more than two

Database on Child Growth and Malnutrition (www.who.int/

standard deviations below the median for the international refer-

nutgrowthdb/en/). Data on under-five mortality rates are from UN

ence population ages 0–59 months. Data are based on the WHO

Inter-agency Group for Child Mortality Estimation (2012) and are

child growth standards released in 2006. • Under-five mortality

based mainly on household surveys, censuses, and vital registra-

rate is the probability of a child born in a specific year dying before

tion data. Modeled estimates of maternal mortality ratios are from

reaching age 5, if subject to the age-specific mortality rates of that

WHO and others (2012). Data on adolescent fertility rates are from

year. The probability is derived from life tables and is expressed as

United Nations Population Division (2011), with annual data linearly

a rate per 1,000 live births. • Maternal mortality ratio, modeled

interpolated by the World Bank’s Development Data Group. Data on

estimate, is the number of women who die from pregnancy-related

HIV prevalence are from UNAIDS (2012). Data on primary completion

causes while pregnant or within 42 days of pregnancy termination,

rates and literacy rates are from the UNESCO Institute for Statistics

per 100,000 live births. • Adolescent fertility rate is the number

(www.uis.unesco.org). Data on labor force participation rates, vul-

of births per 1,000 women ages 15–19. • Prevalence of HIV is the

nerable employment, unemployment, and female legislators, senior

percentage of people who are infected with HIV in the relevant age

officials, and managers are from the ILO’s Key Indicators of the

group. • Primary completion rate is the number of new entrants

Labour Market, 7th edition, database.

(enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age

References

for the last grade of primary education. Data limitations preclude

De Onis, Mercedes, Monika Blössner, Elaine Borghi, Richard Mor-

adjusting for students who drop out during the final year of primary

ris, and Edward A. Frongillo. 2004. “Methodology for Estimating

education. • Youth literacy rate is the percentage of the popula-

Regional and Global Trends of Child Malnutrition.” International Jour-

tion ages 15–24 that can, with understanding, both read and write a short simple statement about their everyday life. • Labor force participation rate is the proportion of the population ages 15 and older that engages actively in the labor market, by either working or

nal of Epidemiology 33: 1260–70. ILO (International Labour Organization).Various years. Key Indicators of the Labour Market. Geneva: International Labour Office. UNAIDS (Joint United Nations Programme on HIV/AIDS). 2012. Global

looking for work during a reference period. • Vulnerable employment

Report: UNAIDS Report on the Global AIDS Epidemic 2012. Geneva.

is unpaid family workers and own-account workers as a percentage

UNICEF (United Nations Children’s Fund), WHO (World Health Organi-

of total employment. • Unemployment is the share of the labor force

zation), and World Bank. 2012. Joint Child Malnutrition Estimates—­

without work but available for and seeking employment. Definitions

Levels and Trends. www.who.int/nutgrowthdb/jme_unicef_who_

of labor force and unemployment may differ by country. • Female legislators, senior officials, and managers are the percentage of legislators, senior officials, and managers (International Standard Classification of Occupations–88 category 1) who are female.

wb.pdf. New York: UNICEF. UN Inter-agency Group for Child Mortality Estimation. 2012. Levels and Trends in Child Mortality: Report 2012. New York. United Nations Population Division. 2011. World Population Prospects: The 2010 Revision. New York: United Nations, Department of Economic and Social Affairs. WHO (World Health Organization), UNICEF (United Nations Children’s Fund), UNFPA (United Nations Population Fund), and World Bank. 2012. Trends in Maternal Mortality: 1990 to 2010. Geneva: WHO. World Bank. n.d. PovcalNet online database. http://iresearch .worldbank.org/PovcalNet. Washington, DC.

46 

World Development Indicators 2013

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People Environment


People 2 Online tables and indicators To access the World Development Indicators online tables, use

indicator online, use the URL http://data.worldbank.org/indicator/

the URL http://wdi.worldbank.org/table/ and the table number (for

and the indicator code (for example, http://data.worldbank.org/

example, http://wdi.worldbank.org/table/2.1). To view a specific

indicator/SP.POP.TOTL).

2.1 Population dynamics Population

SP.POP.TOTL

Population growth

SP.POP.GROW

Population ages 0–14

SP.POP.0014.TO.ZS

Population ages 15–64

SP.POP.1564.TO.ZS

Population ages 65+

SP.POP.65UP.TO.ZS

Dependency ratio, Young

SP.POP.DPND.YG

Dependency ratio, Old

SP.POP.DPND.OL

Crude death rate

SP.DYN.CDRT.IN

Crude birth rate

SP.DYN.CBRT.IN

Unemployment by educational attainment, Secondary

SL.UEM.SECO.ZS

Unemployment by educational attainment, Tertiary

SL.UEM.TERT.ZS

2.6 Children at work Children in employment, Total

SL.TLF.0714.ZS

Children in employment, Male

SL.TLF.0714.MA.ZS

Children in employment, Female

SL.TLF.0714.FE.ZS

Work only

SL.TLF.0714.WK.ZS

Study and work

SL.TLF.0714.SW.ZS

2.2 Labor force structure

Employment in agriculture

SL.AGR.0714.ZS

Labor force participation rate, Male

Employment in manufacturing

SL.MNF.0714.ZS

Employment in services

SL.SRV.0714.ZS

SL.TLF.CACT.MA.ZS

Labor force participation rate, Female Labor force, Total

SL.TLF.CACT.FE.ZS SL.TLF.TOTL.IN

Self-employed

SL.SLF.0714.ZS

..a,b

Wage workers

SL.WAG0714.ZS

Unpaid family workers

SL.FAM.0714.ZS

Labor force, Average annual growth Labor force, Female

SL.TLF.TOTL.FE.ZS

2.3 Employment by sector

2.7 Poverty rates at national poverty lines

Agriculture, Male Agriculture, Female Industry, Male Industry, Female Services, Male Services, Female

SL.AGR.EMPL.MA.ZS

Poverty headcount ratio, Rural

SI.POV.RUHC

SL.AGR.EMPL.FE.ZS

Poverty headcount ratio, Urban

SI.POV.URHC

SL.IND.EMPL.MA.ZS

Poverty headcount ratio, National

SI.POV.NAHC

SL.IND.EMPL.FE.ZS

Poverty gap, Rural

SI.POV.RUGP

SL.SRV.EMPL.MA.ZS

Poverty gap, Urban

SI.POV.URGP

Poverty gap, National

SI.POV.NAGP

SL.SRV.EMPL.FE.ZS

2.4 Decent work and productive employment Employment to population ratio, Total

SL.EMP.TOTL.SP.ZS

Employment to population ratio, Youth

SL.EMP.1524.SP.ZS

Vulnerable employment, Male

SL.EMP.VULN.MA.ZS

Vulnerable employment, Female

SL.EMP.VULN.FE.ZS

GDP per person employed

SL.GDP.PCAP.EM.KD

2.8 Poverty rates at international poverty lines Population living below 2005 PPP $1.25 a day

SI.POV.DDAY

Poverty gap at 2005 PPP $1.25 a day

SI.POV.2DAY

Population living below 2005 PPP $2 a day

SI.POV.GAPS

Poverty gap at 2005 PPP $2 a day

SI.POV.GAP2

2.9 Distribution of income or consumption 2.5 Unemployment

Gini index

Unemployment, Male

SL.UEM.TOTL.MA.ZS

Unemployment, Female

SL.UEM.TOTL.FE.ZS

Youth unemployment, Male

SL.UEM.1524.MA.ZS

Youth unemployment, Female

SL.UEM.1524.FE.ZS

Long-term unemployment, Total

SL.UEM.LTRM.ZS

Long-term unemployment, Male

SL.UEM.LTRM.MA.ZS

Long-term unemployment, Female

SL.UEM.LTRM.FE.ZS

Unemployment by educational attainment, Primary

Economy

SL.UEM.PRIM.ZS

States and markets

SI.POV.GINI

Share of consumption or income, Lowest 10% of population

SI.DST.FRST.10

Share of consumption or income, Lowest 20% of population

SI.DST.FRST.20

Share of consumption or income, Second 20% of population

SI.DST.02ND.20

Share of consumption or income, Third 20% of population

SI.DST.03RD.20

Share of consumption or income, Fourth 20% of population

SI.DST.04TH.20

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World Development Indicators 2013 47


2 People Share of consumption or income, Highest 20% of population

SI.DST.05TH.20

Share of consumption or income, Highest 10% of population

SI.DST.10TH.10

Primary completion rate, Female

SE.ADT.1524.LT.MA.ZS

Youth literacy rate, Female

SE.ADT.1524.LT.FE.ZS

Adult literacy rate, Male

2.10 Education inputs SE.XPD.PRIM.PC.ZS

Public expenditure per student, Secondary

SE.XPD.SECO.PC.ZS

Public expenditure per student, Tertiary

SE.XPD.TERT.PC.ZS

Public expenditure on education, % of GDP

SE.XPD.TOTL.GD.ZS

Public expenditure on education, % of total government expenditure

SE.XPD.TOTL.GB.ZS

Trained teachers in primary education Primary school pupil-teacher ratio

SE.PRM.TCAQ.ZS SE.PRM.ENRL.TC.ZS

2.11 Participation in education Preprimary gross enrollment ratio

SE.PRE.ENRR

Primary gross enrollment ratio

SE.PRM.ENRR

Secondary gross enrollment ratio

SE.SEC.ENRR

Tertiary gross enrollment ratio

SE.TER.ENRR

Primary net enrollment rate

SE.PRM.NENR

Secondary net enrollment rate

SE.SEC.NENR

Primary adjusted net enrollment rate, Male

SE.PRM.TENR.MA

Primary adjusted net enrollment rate, Female

SE.PRM.TENR.FE SE.PRM.UNER.MA

Primary school-age children out of school, Female

SE.PRM.UNER.FE

SE.PRM.GINT.MA.ZS

Gross intake ratio in first grade of primary education, Female Reaching grade 5, Male

SE.PRM.GINT.FE.ZS SE.PRM.PRS5.MA.ZS

Reaching grade 5, Female

SE.PRM.PRS5.FE.ZS

Reaching last grade of primary education, Male

SE.PRM.PRSL.MA.ZS

Reaching last grade of primary education, Female

SE.PRM.PRSL.FE.ZS

Repeaters in primary education, Male

SE.PRM.REPT.MA.ZS

Repeaters in primary education, Female

SE.PRM.REPT.FE.ZS

Transition rate to secondary education, Male

SE.SEC.PROG.MA.ZS

Transition rate to secondary education, Female

SE.SEC.PROG.FE.ZS

2.13 Education completion and outcomes Primary completion rate, Total

SE.PRM.CMPT.ZS

Primary completion rate, Male

World Development Indicators 2013

SE.PRM.CMPT.MA.ZS

Front

2.14 Education gaps by income and gender This table provides education survey data for the poorest and richest quintiles.

?

User guide

..b

2.15 Health systems Total health expenditure

SH.XPD.TOTL.ZS

Public health expenditure

SH.XPD.PUBL

Out-of-pocket health expenditure

SH.XPD.OOPC.ZS

External resources for health

SH.XPD.EXTR.ZS

Health expenditure per capita, $ Health expenditure per capita, PPP $ Physicians

SH.XPD.PCAP SH.XPD.PCAP.PP.KD SH.MED.PHYS.ZS

Nurses and midwives

SH.MED.NUMW.P3

Community health workers

SH.MED.CMHW.P3

Hospital beds

SH.MED.BEDS.ZS

Completeness of birth registration

SP.REG.BRTH.ZS

2.16 Disease prevention coverage and quality Access to improved sanitation facilities Child immunization rate, Measles Child immunization rate, DTP3 Children with acute respiratory infection taken to health provider

2.12 Education efficiency Gross intake ratio in first grade of primary education, Male

SE.ADT.LITR.FE.ZS

Access to an improved water source

Primary school-age children out of school, Male

48 

SE.ADT.LITR.MA.ZS

Adult literacy rate, Female

Public expenditure per student, Primary

SE.PRM.CMPT.FE.ZS

Youth literacy rate, Male

SH.H2O.SAFE.ZS SH.STA.ACSN SH.IMM.MEAS SH.IMM.IDPT SH.STA.ARIC.ZS

Children with diarrhea who received oral rehydration and continuous feeding

SH.STA.ORCF.ZS

Children sleeping under treated bed nets

SH.MLR.NETS.ZS

Children with fever receiving antimalarial drugs

SH.MLR.TRET.ZS

Tuberculosis treatment success rate

SH.TBS.CURE.ZS

Tuberculosis case detection rate

SH.TBS.DTEC.ZS

2.17 Reproductive health Total fertility rate

SP.DYN.TFRT.IN

Adolescent fertility rate

SP.ADO.TFRT

Unmet need for contraception

SP.UWT.TFRT

Contraceptive prevalence rate

SP.DYN.CONU.ZS

Pregnant women receiving prenatal care

SH.STA.ANVC.ZS

Births attended by skilled health staff

SH.STA.BRTC.ZS

Maternal mortality ratio, National estimate

SH.STA.MMRT.NE

Maternal mortality ratio, Modeled estimate

SH.STA.MMRT

Lifetime risk of maternal mortality

SH.MMR.RISK

World view

People Environment


People 2 2.18 Nutrition and growth Prevalence of undernourishment

SN.ITK.DEFC.ZS

Prevalence of underweight, Male

SH.STA.MALN.MA.ZS

Women’s share of population ages 15+ living with HIV

SH.DYN.AIDS.FE.ZS

Prevalence of HIV, Youth male

SH.HIV.1524.MA.ZS

Prevalence of underweight, Female

SH.STA.MALN.FE.ZS

Prevalence of HIV, Youth female

Prevalence of stunting, Male

SH.STA.STNT.MA.ZS

Antiretroviral therapy coverage

Prevalence of stunting, Female

SH.STA.STNT.FE.ZS

Prevalence of wasting, Male

SH.STA.WAST.MA.ZS

Prevalence of wasting, Female

SH.STA.WAST.FE.ZS

Prevalence of overweight children, Male

SH.STA.OWGH.MA.ZS

Prevalence of overweight children, Female

SH.STA.OWGH.FE.ZS

Low-birthweight babies

SH.STA.BRTW.ZS

Exclusive breastfeeding

SH.STA.BFED.ZS

Consumption of iodized salt

SN.ITK.SALT.ZS

Vitamin A supplementation

SN.ITK.VITA.ZS

Prevalence of anemia among children under age 5 Prevalence of anemia among pregnant women

SH.HIV.ARTC.ZS

Death from communicable diseases and maternal, prenatal, and nutrition conditions

SH.DTH.COMM.ZS

Death from non-communicable diseases

SH.DTH.NCOM.ZS

Death from injuries

SH.DTH.INJR.ZS

2.21 Mortality Life expectancy at birth

2.19 Nutrition intake and supplements

SH.ANM.CHLD.ZS

SH.HIV.1524.FE.ZS

SP.DYN.LE00.IN

Neonatal mortality rate

SH.DYN.NMRT

Infant mortality rate

SP.DYN.IMRT.IN

Under-five mortality rate, Total

SH.DYN.MORT

Under-five mortality rate, Male

SH.DYN.MORT.MA

Under-five mortality rate, Female

SH.DYN.MORT.FE

Adult mortality rate, Male

SP.DYN.AMRT.MA

Adult mortality rate, Female

SP.DYN.AMRT.FE

SH.PRG.ANEM

2.22 Health gaps by income 2.20 Health risk factors and future challenges Prevalence of smoking, Male Prevalence of smoking, Female Incidence of tuberculosis

SH.PRV.SMOK.MA

..b

SH.PRV.SMOK.FE SH.TBS.INCD

Prevalence of diabetes

SH.STA.DIAB.ZS

Prevalence of HIV, Total

SH.DYN.AIDS.ZS

Economy

This table provides health survey data for the poorest and richest quintiles.

States and markets

Data disaggregated by sex are available in the World Development Indicators database. a. Derived from data elsewhere in the World Development Indicators database. b. Available online only as part of the table, not as an individual indicator.

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World Development Indicators 2013 49


ENVIRONMENT

50â&#x20AC;&#x192;

World Development Indicators 2013

Front

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User guide

World view

People

Environment


The Millennium Development Goals call for integrating principles of environmental sustainability into country policies and programs and reversing environmental losses. Whether the world continues to sustain itself depends largely on properly managing its natural resources. The indicators in the Environment section measure resource use and the way human activities affect the natural and built environment. They include measures of environmental goods (forest, water, cultivatable land) and of degradation (pollution, deforestation, loss of habitat, and loss of biodiversity). These indicators show that growing populations and expanding economies have placed greater demands on land, water, forests, minerals, and energy sources. But new technologies, increasing productivity, and better policies can ensure that future development is environmentally and socially sustainable. Nowhere are these risks and opportunities more intertwined than in the global effort to mitigate the effects of climate change. A continuing rise in temperature, accompanied by changes in precipitation patterns, is projected for this century, as are more frequent, severe, and prolonged climate-related events such as floods and droughts—posing risks for agriculture, food production, and water supplies. Poor countries and the poorest people in all countries are most vulnerable to the changing climate. The 2012 edition of World Development Indicators included two new tables on climate

Economy

States and markets

change. The first presents data on carbon dioxide emissions by economic sector from the International Energy Agency’s annual time series statistics. And the second contains country indicators on climate variability, exposure to impact, and resilience. Other indicators in this section describe land use, agriculture and food production, forests and biodiversity, threatened species, water resources, energy use and efficiency, electricity production and use, greenhouse gas emissions, urbanization, traffic and congestion, air pollution, government commitments, and natural resource rents. Where possible, the indicators come from international sources and are standardized to facilitate comparison across countries. But ecosystems span national boundaries, and access to natural resources may vary within countries. For example, water may be abundant in some parts of a country but scarce in others, and countries often share water resources. Land productivity and optimal land use may be location specific, but widely separated regions can have common characteristics. Greenhouse gas emissions and climate change are measured globally, but their effects are experienced locally, shaping people’s lives and opportunities. Measuring environmental phenomena and their effects at the subnational, national, and supranational levels remains a major challenge for achieving longterm, sustainable development.

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World Development Indicators 2013 51


Highlights East Asia & Pacific: More access to improved sanitation facilities Share of population with access to improved sanitation facilities (%) 100

East Asia and Pacific has more than doubled the proportion of people with access to improved sanitation facilities. This is an impressive

Samoa

achievement, bringing access to basic sanitation facilities to more

Tonga

than 700 million additional people, mostly in China. Because of its

80

size, China dominates the regional average of East Asia and Pacific. Palau

But some countries progressed even faster than China, such as

East Asia & Pacific

60

Palau, with 100 percent access in 2010. At the other end of the

China

Papua New Guinea

spectrum is Cambodia, where only 31 percent of the population has

40

access.

Timor-Leste 20 Cambodia 0

1990

1995

2000

2005

2010

Source: Table 3.

Europe & Central Asia: Emissions fall but per capita carbon dioxide emissions remain high Carbon dioxide emissions (metric tons per capita)

Carbon dioxide emissions, largely a byproduct of energy production and use, account for the largest portion of greenhouse gases released

30

each year. Greenhouse gases—including carbon dioxide, methane, nitrous oxide, and other industrial gases (hydrofluorocarbons, perfluo20

rocarbons, and sulfur hexafluoride)—are associated with global warming and environmental damage. In 2009 the world released an estimated 32 billion metric tons of carbon dioxide, up 44 percent from

10

cent over the same period, but emissions per capita remain the highest

Lu xe mb ou Fa rg ero eI sla nd s Ka za kh sta n

Ru Es ss ton ian ia Fe de r a Cz tio ec n hR ep ub l ic Ne the rla nd s Gr ee nla nd Fin lan d Eu rop e & Norw ay Ce ntr al As ia a

1990 2009

0

1990. In Europe and Central Asia carbon dioxide emissions fell 25 peramong developing regions. Emissions of other greenhouse gases have also risen over the last two decades. In 2010 global emissions were estimated at 7.5 billion metric tons of carbon dioxide equivalent for methane, 2.9 billion for nitrous dioxide, and 1.0 billion for other industrial gases.

a. Includes countries at all income levels. Source: Online table 3.8.

Latin America & Caribbean: The leader in clean and efficient energy Latin America and the Caribbean remains one of the world’s most efficient

Energy use, 2010 (%)

energy-using regions, measured by the ratio of gross domestic product

100

to energy use. Latin American countries averaged $7.70 of output per kilogram of oil-equivalent energy used in 2010 (in 2005 purchasing

75

power parity dollars), up 12 percent from 1990. Peru, Colombia, Panama, Costa Rica, and Uruguay were the region’s most efficient energy users. Clean energy from noncarbon energy sources, which consists of

50

alternative energy (geothermal, solar, and hydropower) and nuclear energy, is also on the rise, accounting for 9.2 percent of world energy 25

0

use in 2010, but 10.4 percent in Latin America and the Caribbean, high-

Alternative and nuclear energy Combustible renewables and waste Fossil fuel Latin America & Caribbean

Europe & Central Asia

East Asia & Pacific

est among developing regions. The increase in carbon dioxide emissions slowed, but the region’s emissions still rose more than 57 percent South Asia

Sub-Saharan Middle East & North Africa Africa

between 1990 and 2009. Mexico, Brazil, República Bolivariana de Venezuela, Argentina, and Colombia were the largest emitters.

Source: Online tables 3.6 and 3.8.

52 

World Development Indicators 2013

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People

Environment


Middle East & North Africa: The most water-stressed region The world has about 42 trillion cubic meters of available freshwater,

Renewable internal freshwater resources, 2011 (%)

but distribution is drastically uneven. The Middle East and North South Asia 5%

Africa is the most water-stressed region, with less than 1 percent of global renewable freshwater resources. At 226 billion cubic meters,

Middle East & North Africa 0.5%

SubSaharan Africa 9%

the region has only 673 cubic meters of water per person, the lowest among developing regions. By contrast, Latin America and the Carib-

Latin America & Caribbean 32%

Europe & Central Asia 12%

bean, with 32 percent of world resources, has 22,810 cubic meters per person; Europe and Central Asia, with 12 percent, has 12,516 cubic meters per person; and East Asia and Pacific, with 21 percent, has

East Asia & Pacific 21%

4,446 cubic meters per person.

High income 21%

Source: Online table 3.5.

South Asia: Some of the world’s most polluted cities South Asia has some of the most polluted air in the world, as measured by the concentration of fine suspended particulates of

Particulates of less than 10 microns in diameter, 2010 (micrograms per cubic meter) Nyala, Sudan

fewer than 10 microns in diameter (PM10), capable of penetrating

Maroua, Cameroon

deep into the respiratory tract and causing severe health damage.

Dhaka, Bangladesh

The PM10 estimates measure the annual exposure of the average

Muzaffarpur, India

urban resident to outdoor particulate matter. Globally, air pollution

Hyderabad, Pakistan Saint-Louis, Senegal

has fallen from 78 micrograms per cubic meter to 41 over the last

Ségou, Mali

two decades. Among developing regions the highest concentrations

Xi’an, China

are in South Asia (62) and the Middle East and North Africa (59).

Montevideo, Uruguay Bamako, Mali

City-level PM10 concentration data, however, indicate that Sub-­

Niamey, Niger

Saharan Africa has the most cities with high levels of pollution

Osh, Kyrgyz Republic

among developing regions. Even so, the PM10 concentration has

Shubra-El-Khema, Egypt

dropped significantly in Sub-­S aharan Africa, falling 65 percent over

N’djamena, Chad 0

1990–2010.

50

100

150

200

Source: Online tables 3.13 and 3.14.

Sub-­Saharan Africa: Use of biomass energy for cooking and heating increases health risks Combustible renewables and waste, 2010 (% of energy use)

Many poor people depend on biomass energy from plant materials or

100

animal waste for cooking and heating. Millions of deaths are caused by indoor air pollution each year, due largely to indoor particulate pollution. Many are children, who die of acute respiratory infections from

75

burning fuel wood, crop residues, or animal dung (WHO 2004). These sources of energy account for 66 percent of total energy used by more

50

than 800 million low-income inhabitants of the world. In Sub-­Saharan Africa use of combustible renewables and waste, which account for

25

more than half of total energy use, has risen almost 3 percent over

on the rise.

Mo Togo za mb iqu e Za mb ia Eri Cô trea te d’I vo ire Su bK en Sa ya ha ran Af ric a Wo rld

of biomass and coal—the primary cooking and heating fuel—is still

0

De m. Re p. Eth iop ia Ta nz an ia Ni ge ria

of the population lacks access to any form of electrical services, use

Co ng o,

the last two decades. For this region, where an estimated 67 percent

Source: Online tables 1.1 and 3.6.

Economy

States and markets

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World Development Indicators 2013 53


3 Environment Deforestationa Nationally protected areas

average annual %

Afghanistan Albania

Access to improved water source

Access to Urban Particulate Carbon improved population matter dioxide sanitation concentration emissions facilities urban-population-

Energy use Electricity production

marine areas % of total Per capita territorial area cubic meters

% of total population

% of total population

average annual % growth

weighted PM10 micrograms per cubic meter

million metric tons

Per capita kilograms of oil equivalent

billion kilowatt hours

2009

2010

2010

2000–10

2010

2011

2010

2010

1990–2011

2010

0.00

0.4

1,335

50

37

4.0

30

6.3

..

..

–0.10

8.4

8,364

95

94

2.4

38

3.0

648

7.6

Algeria

0.57

6.2

313

83

95

2.6

69

121.3

1,138

45.6

American Samoa

0.19

16.7

..

..

..

1.9

..

..

..

..

Andorra

0.00

6.1

3,663

100

100

0.9

18

0.5

..

..

Angola

0.21

12.1

7,544

51

58

4.1

58

26.7

716

5.3

Antigua and Barbuda

0.20

1.0

580

..

..

1.0

13

0.5

..

..

Argentina

0.81

5.3

6,771

..

..

1.0

57

174.7

1,847

125.3

Armenia

1.48

8.0

2,212

98

90

0.3

45

4.5

791

6.5

Aruba

0.00

0.0

..

100

..

0.8

..

2.3

..

..

Australia

0.37

12.5

22,039

100

100

1.3

13

400.2

5,653

241.5

–0.13

22.9

6,529

100

100

0.7

27

62.3

4,034

67.9

0.00

7.1

885

80

82

1.8

27

49.1

1,307

18.7

Austria Azerbaijan Bahamas, The

0.00

1.0

58

..

100

1.5

..

2.6

..

..

–3.55

0.7

3

..

..

4.9

44

24.2

7,754

13.2

Bangladesh

0.18

1.6

698

81

56

3.0

115

51.0

209

42.3

Barbados

0.00

0.1

292

100

100

1.4

35

1.6

..

..

Belarus

–0.43

7.2

3,927

100

93

0.4

6

60.3

2,922

34.9

Belgium

93.8

Bahrain

–0.16

13.2

1,089

100

100

1.2

21

103.6

5,586

Belize

0.67

20.6

44,868

98

90

3.0

12

0.4

..

..

Benin

1.04

23.3

1,132

75

13

4.2

48

4.9

413

0.2 ..

Bermuda

0.00

5.1

..

..

..

0.7

..

0.5

..

Bhutan

–0.34

28.3

105,653

96

44

3.9

20

0.4

..

..

Bolivia

0.50

18.5

30,085

88

27

2.2

57

14.5

737

6.9 17.1

Bosnia and Herzegovina

0.00

0.6

9,461

99

95

0.9

21

30.1

1,703

Botswana

0.99

30.9

1,182

96

62

2.2

64

4.4

1,128

0.5

Brazil

0.50

26.0

27,551

98

79

1.2

18

367.1

1,363

515.7

Brunei Darussalam

0.44

29.6

20,939

..

..

2.2

44

9.3

8,308

3.9

–1.53

8.9

2,858

100

100

–1.7

40

42.8

2,370

46.0

Burkina Faso

1.01

14.2

737

79

17

6.2

65

1.7

..

..

Burundi

1.40

4.8

1,173

72

46

4.9

24

0.2

..

..

Cambodia

1.34

23.4

8,431

64

31

2.1

42

4.6

355

1.0

Cameroon

1.05

9.0

13,629

77

49

3.3

59

6.7

363

5.9

Canada

0.00

6.2

82,647

100

100

1.2

15

513.9

7,380

607.8

Bulgaria

Cape Verde

–0.36

0.2

599

88

61

2.1

..

0.3

..

..

Cayman Islands

0.00

1.5

..

96

96

0.9

..

0.5

..

..

Central African Republic

0.13

17.7

31,425

67

34

2.6

35

0.2

..

..

Chad

0.66

9.4

1,301

51

13

3.0

83

0.4

..

..

..

0.5

..

..

..

0.8

..

..

..

..

Chile

–0.25

13.3

51,188

96

96

1.1

46

66.7

1,807

60.4

China

Channel Islands

54 

Internal renewable freshwater b Terrestrial and resources

–1.57

16.0

2,093

91

64

3.0

59

7,687.1

1,807

4,208.3

Hong Kong SAR, China

..

41.8

..

..

..

0.1

..

37.0

1,951

38.3

Macao SAR, China

..

..

..

..

..

2.2

..

1.5

..

..

Colombia

0.17

20.5

45,006

92

77

1.7

19

71.2

696

56.8

Comoros

9.34

..

1,592

95

36

2.9

30

0.1

..

..

Congo, Dem. Rep.

0.20

10.0

13,283

45

24

4.3

35

2.7

360

7.9

Congo, Rep.

0.07

9.7

53,626

71

18

3.0

57

1.9

363

0.6

World Development Indicators 2013

Front

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People

Environment


Environment 3 Deforestationa Nationally protected areas

average annual %

Internal renewable freshwater b Terrestrial and resources

Access to improved water source

marine areas % of total Per capita territorial area cubic meters

% of total population

% of total population

average annual % growth

weighted PM10 micrograms per cubic meter

million metric tons

Per capita kilograms of oil equivalent

billion kilowatt hours

2009

2010

2010

Energy use Electricity production

2000–10

2010

2010

2010

1990–2011

2010

Costa Rica

–0.93

17.6

23,780

97

95

2.2

27

8.3

998

9.6

Côte d’Ivoire

–0.15

21.8

3,813

80

24

3.5

30

6.6

485

6.0

Croatia

–0.19

9.5

8,562

99

99

0.2

22

21.5

1,932

14.0

Cuba

–1.66

5.3

3,387

94

91

–0.1

15

31.6

975

17.4

..

..

..

..

..

..

..

..

..

..

Cyprus

–0.09

4.5

699

100

100

1.4

27

8.2

2,215

5.4

Czech Republic

–0.08

15.1

1,253

100

98

–0.3

16

108.1

4,193

85.3

Denmark

–1.14

4.1

1,077

100

100

0.6

15

45.7

3,470

38.8

Djibouti

0.00

0.0

331

88

50

2.0

28

0.5

..

..

Dominica

0.58

3.7

..

..

..

0.1

20

0.1

..

..

Dominican Republic

0.00

24.1

2,088

86

83

2.1

14

20.3

840

15.9

1.81

38.0

29,456

94

92

2.2

19

30.1

836

17.7

–1.73

6.1

22

99

95

2.1

78

216.1

903

146.8 6.0

Curaçao

Ecuador Egypt, Arab Rep.

2011

Access to Urban Particulate Carbon improved population matter dioxide sanitation concentration emissions facilities urban-population-

El Salvador

1.45

1.4

2,850

88

87

1.3

28

6.3

677

Equatorial Guinea

0.69

14.0

36,100

..

..

3.2

6

4.8

..

..

Eritrea

0.28

3.8

517

61

14

5.2

61

0.5

142

0.3

Estonia

0.12

22.6

9,486

98

95

0.1

9

16.0

4,155

13.0

Ethiopia

1.08

18.4

1,440

44

21

3.7

47

7.9

400

5.0

Faeroe Islands

0.00

..

..

..

..

0.8

11

0.7

..

..

–0.34

0.2

32,876

98

83

1.7

20

0.8

..

..

Finland

0.14

8.5

19,858

100

100

0.6

15

53.6

6,787

80.7

France

–0.39

17.1

3,057

100

100

1.2

12

363.4

4,031

564.3

French Polynesia

–3.97

0.1

..

100

98

1.1

..

0.9

..

..

0.00

14.6

106,892

87

33

2.3

7

1.6

1,418

1.8

–0.41

1.3

1,689

89

68

3.7

60

0.4

..

..

0.09

3.4

12,958

98

95

1.0

49

5.8

700

10.1

Germany

0.00

42.3

1,308

100

100

0.2

16

734.6

4,003

622.1

Ghana

2.08

14.0

1,214

86

14

3.6

22

7.4

382

8.4

Greece

–0.81

9.9

5,133

100

98

0.3

27

94.9

2,440

57.4

Greenland

0.00

40.1

..

100

100

0.2

..

0.6

..

..

Grenada

0.00

0.1

..

..

97

1.3

19

0.2

..

..

Guam

0.00

3.6

..

100

99

1.3

..

..

..

..

Guatemala

1.40

29.5

7,400

92

78

3.4

51

15.2

713

8.8

Guinea

0.54

6.4

22,110

74

18

3.8

55

1.2

..

..

Guinea-Bissau

0.48

26.9

10,342

64

20

3.6

48

0.3

..

..

Guyana

0.00

4.8

318,766

94

84

0.5

20

1.6

..

..

Haiti

0.76

0.1

1,285

69

17

3.8

35

2.3

229

0.6

Fiji

Gabon Gambia, The Georgia

Honduras

2.06

13.9

12,371

87

77

3.1

34

7.7

601

6.7

Hungary

–0.62

5.1

602

100

100

0.4

15

48.7

2,567

37.4

Iceland

–4.99

13.2

532,892

100

100

0.4

18

2.0

16,882

17.1

India

–0.46

4.8

1,165

92

34

2.5

52

1,979.4

566

959.9

Indonesia

0.51

6.4

8,332

82

54

2.5

60

451.8

867

169.8

Iran, Islamic Rep.

0.00

6.9

1,718

96

100

1.3

56

602.1

2,817

233.0

Iraq

–0.09

0.1

1,068

79

73

2.8

88

109.0

1,180

50.2

Ireland

–1.53

1.2

10,707

100

99

2.7

13

41.6

3,218

28.4

0.00

..

..

..

..

0.5

..

..

..

..

–0.07

15.1

97

100

100

1.9

21

67.2

3,005

58.6

Isle of Man Israel

Economy

States and markets

Global links

Back

World Development Indicators 2013 55


3 Environment Deforestationa Nationally protected areas

average annual %

Access to improved water source

Access to Urban Particulate Carbon improved population matter dioxide sanitation concentration emissions facilities urban-population-

marine areas % of total Per capita territorial area cubic meters

% of total population

% of total population

average annual % growth

weighted PM10 micrograms per cubic meter

million metric tons

Energy use Electricity production

Per capita kilograms of oil equivalent

billion kilowatt hours

2000–10

2010

2011

2010

2010

1990–2011

2010

2009

2010

2010

–0.90

15.9

3,005

100

..

0.7

21

400.8

2,815

298.8

0.11

7.3

3,475

93

80

0.4

27

8.6

1,131

4.2

Japan

–0.05

10.9

3,364

100

100

0.9

24

1,101.1

3,898

1,110.8

Jordan

0.00

1.9

110

97

98

2.5

30

22.5

1,191

14.8

Kazakhstan

0.17

2.5

3,886

95

97

1.3

18

225.8

4,595

82.6

Kenya

0.33

11.7

497

59

32

4.4

30

12.4

483

7.5

Kiribati

0.00

22.6

..

..

..

1.8

..

0.1

..

..

Korea, Dem. Rep.

2.00

3.9

2,740

98

80

0.6

52

75.1

761

21.7

Korea, Rep.

496.7

Italy Jamaica

0.11

3.0

1,303

98

100

1.1

30

509.4

5,060

Kosovo

..

..

..

..

..

..

..

..

1,372

5.2

Kuwait

–2.57

1.1

0

99

100

2.9

91

80.2

12,204

57.0

Kyrgyz Republic

–1.07

6.9

8,873

90

93

1.5

35

6.7

536

11.4

0.49

16.6

30,280

67

63

4.7

45

1.8

..

..

Latvia

–0.34

16.4

8,133

99

78

–8.4

12

6.7

1,971

6.6

Lebanon

–0.45

0.4

1,127

100

..

0.9

25

21.0

1,526

15.7

Lesotho

–0.47

0.5

2,384

78

26

3.7

38

..

..

..

Liberia

0.67

1.6

48,443

73

18

4.1

31

0.5

..

..

Libya

0.00

0.1

109

..

97

1.3

65

62.9

3,013

31.6

Lao PDR

Liechtenstein Lithuania Luxembourg

0.00

42.4

..

..

..

0.5

24

..

..

..

–0.68

14.4

5,135

92

86

–8.0

16

12.8

2,107

5.0

0.00

20.0

1,930

100

100

2.5

12

10.1

8,343

3.2

–0.41

4.9

2,616

100

88

0.4

17

11.3

1,402

7.3

Madagascar

0.45

2.5

15,810

46

15

4.8

28

1.8

..

..

Malawi

0.97

15.0

1,049

83

51

4.1

29

1.1

..

..

Malaysia

0.54

13.7

20,098

100

96

2.5

18

198.3

2,558

125.3

Maldives

0.00

..

94

98

97

4.1

28

1.0

..

..

Mali

0.61

2.4

3,788

64

22

4.9

111

0.6

..

..

Malta

0.00

1.7

121

100

100

0.1

..

2.5

2,013

2.1

Macedonia, FYR

Marshall Islands

0.00

0.6

..

94

75

1.9

..

0.1

..

..

Mauritania

2.66

1.1

113

50

26

3.0

68

2.1

..

..

Mauritius

1.00

0.7

2,139

99

89

0.4

16

3.8

..

..

Mexico

0.30

11.9

3,563

96

85

1.6

30

446.2

1,570

271.0

Micronesia, Fed. Sts.

–0.04

0.1

..

..

..

0.9

..

0.1

..

..

Moldova

–1.77

1.4

281

96

85

1.4

36

4.5

731

3.6

Monaco

0.00

98.1

..

100

100

0.1

..

..

..

..

Mongolia

0.73

13.4

12,428

82

51

2.9

96

14.5

1,189

4.5

Montenegro

0.00

11.5

..

98

90

0.4

..

3.1

1,303

4.2

–0.23

1.5

899

83

70

1.6

23

48.8

517

22.3

Mozambique

0.54

14.8

4,191

47

18

3.1

22

2.6

436

16.7

Myanmar

0.93

5.2

20,750

83

76

2.5

40

11.1

292

7.5

Namibia

0.97

14.7

2,651

93

32

3.3

42

3.6

702

1.5

Morocco

Nepal Netherlands New Caledonia

0.70

17.0

6,501

89

31

3.8

27

3.5

341

3.2

–0.14

15.2

659

100

100

0.9

30

169.7

5,021

118.1

0.00

23.9

..

..

..

1.4

48

3.0

..

..

–0.01

20.0

74,230

100

..

0.9

11

32.1

4,166

44.8

Nicaragua

2.01

36.8

32,318

85

52

1.9

21

4.5

542

3.7

Niger

0.98

7.1

218

49

9

5.0

96

1.2

..

..

New Zealand

56 

Internal renewable freshwater b Terrestrial and resources

World Development Indicators 2013

Front

?

User guide

World view People view People Environment


Environment 3 Deforestationa Nationally protected areas

average annual %

Internal renewable freshwater b Terrestrial and resources

Access to improved water source

Access to Urban Particulate Carbon improved population matter dioxide sanitation concentration emissions facilities urban-population-

Energy use Electricity production

marine areas % of total Per capita territorial area cubic meters

% of total population

% of total population

average annual % growth

weighted PM10 micrograms per cubic meter

million metric tons

Per capita kilograms of oil equivalent

billion kilowatt hours

2009

2010

2010

2000–10

2010

2011

2010

2010

1990–2011

2010

Nigeria

3.67

12.6

1,360

58

31

3.8

38

70.2

714

Northern Mariana Islands

0.53

28.4

..

98

..

0.5

..

..

..

..

–0.80

10.9

77,124

100

100

1.6

16

47.1

6,637

124.1

Oman

0.00

9.3

492

89

99

2.6

95

41.1

7,188

19.8

Pakistan

2.24

9.8

311

92

48

2.7

91

161.2

487

94.5

Norway

Palau

26.1

–0.18

4.8

..

85

100

1.6

..

0.2

..

..

Panama

0.36

11.5

41,275

93

69

2.3

45

7.8

1,073

7.5

Papua New Guinea

0.48

1.4

114,203

40

45

2.8

16

3.5

..

..

Paraguay

0.97

5.4

14,311

86

71

2.6

64

4.5

742

54.1 35.9

Peru

0.18

13.1

54,966

85

71

1.5

42

47.4

667

Philippines

–0.75

5.0

5,050

92

74

2.2

17

68.6

434

67.7

Poland

–0.31

21.8

1,391

..

90

0.8

33

298.9

2,657

157.1

Portugal

–0.11

6.1

3,600

99

100

0.1

18

57.4

2,213

53.7

Puerto Rico

–1.76

4.4

1,915

..

..

–0.3

15

..

..

..

0.00

1.4

30

100

100

6.3

20

70.3

12,799

28.1

Qatar Romania

–0.32

7.8

1,978

89

73

–0.2

11

79.5

1,632

60.3

0.00

9.2

30,169

97

70

0.6

15

1,574.4

4,927

1,036.1

Rwanda

–2.38

10.0

868

65

55

4.6

21

0.7

..

..

Samoa

0.00

1.2

..

96

98

–0.5

..

0.2

..

..

San Marino

0.00

..

..

..

..

0.7

8

..

..

..

São Tomé and Príncipe

0.00

..

12,936

89

26

2.9

28

0.1

..

..

Saudi Arabia

0.00

29.9

85

..

..

2.5

96

432.8

6,168

240.1

0.49

23.5

2,021

72

52

3.4

77

4.6

272

3.0

–0.99

6.0

1,158

99

92

0.2

..

46.3

2,141

37.4 ..

Russian Federation

Senegal Serbia Seychelles

0.00

0.9

..

..

..

0.1

..

0.7

..

Sierra Leone

0.69

4.3

26,678

55

13

3.2

39

1.4

..

..

Singapore

0.00

3.4

116

100

100

2.1

23

31.9

6,456

45.4

Sint Maarten

..

..

..

..

..

..

..

..

..

..

Slovak Republic

–0.06

23.2

2,334

100

100

–0.7

13

33.9

3,280

27.5

Slovenia

–0.16

13.1

9,095

99

100

0.1

26

15.3

3,520

16.2

Solomon Islands

0.25

0.1

80,939

..

..

4.8

31

0.2

..

..

Somalia

1.07

0.5

628

29

23

3.6

26

0.6

..

..

South Africa

0.00

6.9

886

91

79

1.9

18

499.0

2,738

256.6

South Sudan

..

..

..

..

..

4.7

..

..

..

..

–0.68

7.6

2,408

100

100

0.4

24

288.2

2,773

299.9

Sri Lanka

1.12

15.0

2,530

91

92

1.6

65

12.7

478

10.8

St. Kitts and Nevis

0.00

0.8

452

99

96

1.5

15

0.3

..

..

St. Lucia

–0.07

2.0

..

96

65

–2.6

31

0.4

..

..

St. Martin

0.00

..

..

..

..

..

..

..

..

..

–0.27

1.2

..

..

..

0.8

22

0.2

..

..

0.08

4.2

672

58

26

2.6c

137

14.3

371

7.8

Suriname

0.01

12.2

166,220

92

83

1.5

20

2.5

..

..

Swaziland

–0.84

3.0

2,472

71

57

1.0

30

1.0

..

..

Sweden

–0.30

10.0

18,097

100

100

0.9

10

43.7

5,468

148.5

Switzerland

–0.38

24.9

5,106

100

100

1.2

20

41.6

3,349

66.1

Syrian Arab Republic

–1.29

0.6

343

90

95

2.5

54

65.3

1,063

46.4

0.00

4.1

9,096

64

94

1.6

29

2.8

336

16.4

Spain

St. Vincent and Grenadines Sudan

Tajikistan

Economy

States and markets

Global links

Back

World Development Indicators 2013 57


3 Environment Deforestationa Nationally protected areas

average annual %

Internal renewable freshwater b Terrestrial and resources

Access to improved water source

Access to Urban Particulate Carbon improved population matter dioxide sanitation concentration emissions facilities urban-population-

Energy use Electricity production

marine areas % of total Per capita territorial area cubic meters

% of total population

% of total population

average annual % growth

weighted PM10 micrograms per cubic meter

million metric tons

Per capita kilograms of oil equivalent

billion kilowatt hours

2009

2010

2010

2000–10

2010

2011

2010

2010

1990–2011

2010

Tanzania

1.13

26.9

1,817

53

10

4.8

19

7.0

448

4.4

Thailand

0.02

17.3

3,229

96

96

1.7

53

271.7

1,699

159.5

Timor-Leste

1.40

6.4

6,986

69

47

4.2

..

0.2

..

..

Togo

5.13

11.0

1,868

61

13

3.4

27

1.5

446

0.1

Tonga

0.00

9.4

..

100

96

0.9

..

0.2

..

..

Trinidad and Tobago

0.32

9.6

2,852

94

92

2.3

97

47.8

15,913

8.5

Tunisia

–1.86

1.3

393

94

85

1.5

23

25.2

913

16.1

Turkey

–1.11

1.9

3,083

100

90

2.5

35

277.8

1,445

211.2

Turkmenistan

0.00

3.0

275

..

98

1.9

36

48.2

4,226

16.7

Turks and Caicos Islands

0.00

3.5

..

100

..

2.6

..

0.2

..

..

Tuvalu

0.00

0.2

..

98

85

1.0

..

..

..

..

Uganda

2.56

10.3

1,130

72

34

5.9

10

3.5

..

..

Ukraine

–0.21

3.6

1,162

98

94

–0.1

15

272.2

2,845

188.6

United Arab Emirates

–0.24

4.7

19

100

98

5.3

89

156.8

8,271

97.7

United Kingdom

–0.31

18.1

2,311

100

100

0.9

13

474.6

3,252

378.0

United States

–0.13

13.7

9,044

99

100

1.0

18

5,299.6

7,164

4,354.4

Uruguay

–2.14

0.3

17,515

100

100

0.5

112

7.9

1,241

10.8

Uzbekistan

–0.20

2.3

557

87

100

2.8

31

116.5

1,533

51.7

Vanuatu

0.00

0.5

..

90

57

3.7

14

0.1

..

..

Venezuela, RB

0.60

50.2

24,674

..

..

1.7

10

184.8

2,669

118.3

Vietnam

–1.65

4.6

4,092

95

76

3.1

54

142.3

681

94.9

Virgin Islands (U.S.)

0.80

1.5

..

..

..

0.1

..

..

..

..

West Bank and Gaza

–0.10

0.6

207

85

92

3.3

..

2.2

..

..

0.00

0.7

85

55

53

4.9

34

24.0

298

7.8

Zambia

0.33

36.0

5,952

61

48

5.3

27

2.0

628

11.3

Zimbabwe

1.88

28.0

961

80

40

2.7

34

8.9

764

8.1

0.11 w

11.9 w

88 w

63 w

2.1

Yemen, Rep.

World

6,115 s

w

41 w

32,042.2d w

1,851 w 21,448.9 w

Low income

0.61

10.0

5,125

65

37

3.6

54

229.8

363

197.4

Middle income

0.08

12.0

5,819

90

59

2.3

46

17,344.8

1,310

10,122.1 2,138.2

Lower middle income

0.31

8.8

3,121

87

47

2.6

53

3,884.9

667

Upper middle income

0.02

13.1

8,550

93

73

2.1

42

13,460.8

1,948

7,981.7

Low & middle income

0.15

11.6

5,722

86

56

2.4

47

17,574.2

1,210

10,344.7

East Asia & Pacific

–0.44

13.3

4,446

90

66

2.9

55

8,936.9

1,520

4,888.8

Europe & Central Asia

–0.04

7.7

12,498

96

84

0.9

21

2,863.0

3,015

1,884.3

Latin America & Carib.

0.45

19.8

22,810

94

79

1.5

28

1,533.7

1,312

1,356.4

Middle East & N. Africa

–0.15

4.0

673

89

88

2.1

59

1,320.9

1,372

638.4

South Asia

–0.29

5.6

1,197

90

38

2.7

62

2,215.6

519

1,120.1

0.48

11.6

4,455

61

31

3.8

41

724.0

683

441.4

High income

Sub-­Saharan Africa

–0.04

12.7

8,195

100

100

1.0

22

12,727.0

5,000

11,163.7

Euro area

–0.31

16.5

2,933

100

100

0.6

18

2,456.1

3,633

2,352.4

a. Negative values indicate an increase in forest area. b. River flows from other countries are not included because of data unreliability. c. Excludes South Sudan. d. Includes emissions not allocated to specific countries.

58 

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Environment 3 About the data Environmental resources are needed to promote growth and poverty

collected intermittently and may hide substantial year-to-year varia-

reduction, but growth can create new stresses on the environment.

tions in total renewable water resources. Data do not distinguish

Deforestation, loss of biologically diverse habitat, depletion of water

between seasonal and geographic variations in water availability

resources, pollution, urbanization, and ever increasing demand for

within countries. Data for small countries and countries in arid and

energy production are some of the factors that must be considered

semiarid zones are less reliable than data for larger countries and

in shaping development strategies.

countries with greater rainfall.

Loss of forests

Water and sanitation

Forests provide habitat for many species and act as carbon sinks.

A reliable supply of safe drinking water and sanitary disposal of

If properly managed they also provide a livelihood for people who

excreta are two of the most important means of improving human

manage and use forest resources. FAO (2010) uses a uniform defi-

health and protecting the environment. Improved sanitation facilities

nition of forest to provide information on forest cover in 2010 and

prevent human, animal, and insect contact with excreta.

adjusted estimates of forest cover in 1990 and 2000. Data pre-

Data on access to an improved water source measure the percent-

sented here do not distinguish natural forests from plantations, a

age of the population with ready access to water for domestic pur-

breakdown the FAO provides only for developing countries. Thus,

poses, based on surveys and estimates of service users provided

data may underestimate the rate at which natural forest is disap-

by governments to the Joint Monitoring Programme of the World

pearing in some countries.

Health Organization (WHO) and the United Nations Children’s Fund (UNICEF). The coverage rates are based on information from service

Habitat protection and biodiversity

users on household use rather than on information from service

Deforestation is a major cause of loss of biodiversity, and habitat

providers, which may include nonfunctioning systems. Access to

conservation is vital for stemming this loss. Conservation efforts

drinking water from an improved source does not ensure that the

have focused on protecting areas of high biodiversity. The World

water is safe or adequate, as these characteristics are not tested

Conservation Monitoring Centre (WCMC) and the United Nations

at the time of survey. While information on access to an improved

Environment Programme (UNEP) compile data on protected areas.

water source is widely used, it is extremely subjective; terms such as

Differences in definitions, reporting practices, and reporting peri-

“safe,” “improved,” “adequate,” and “reasonable” may have differ-

ods limit cross-country comparability. Nationally protected areas

ent meanings in different countries despite official WHO definitions

are defined using the six International Union for Conservation of

(see Definitions). Even in high-income countries treated water may

Nature (IUCN) categories for areas of at least 1,000 hectares—

not always be safe to drink. Access to an improved water source is

scientific reserves and strict nature reserves with limited public

equated with connection to a supply system; it does not account for

access, national parks of national or international significance and

variations in the quality and cost of the service.

not materially affected by human activity, natural monuments and natural landscapes with unique aspects, managed nature reserves

Urbanization

and wildlife sanctuaries, protected landscapes (which may include

There is no consistent and universally accepted standard for distin-

cultural landscapes), and areas managed mainly for the sustainable

guishing urban from rural areas and, by extension, calculating their

use of natural systems to ensure long-term protection and mainte-

populations. Most countries use a classification related to the size

nance of biological diversity—as well as terrestrial protected areas

or characteristics of settlements. Some define areas based on the

not assigned to an IUCN category. Designating an area as protected

presence of certain infrastructure and services. Others designate

does not mean that protection is in force. For small countries with

areas based on administrative arrangements. Because data are

protected areas smaller than 1,000 hectares, the size limit in the

based on national definitions, cross-country comparisons should

definition leads to underestimation of protected areas. Due to varia-

be made with caution.

tions in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information

Air pollution

more frequently than others, some have more accurate data on

Indoor and outdoor air pollution place a major burden on world

extent of coverage, and many underreport the number or extent of

health. More than half the world’s people rely on dung, wood, crop

protected areas.

waste, or coal to meet basic energy needs. Cooking and heating with these fuels on open fires or stoves without chimneys lead to indoor

Freshwater resources

air pollution, which is responsible for 1.6 million deaths a year—one

The data on freshwater resources are derived from estimates of

every 20 seconds. In many urban areas air pollution exposure is the

runoff into rivers and recharge of groundwater. These estimates

main environmental threat to health. Long-term exposure to high

are derived from different sources and refer to different years, so

levels of soot and small particles contributes to such health effects

cross-country comparisons should be made with caution. Data are

as respiratory diseases, lung cancer, and heart disease. Particulate

Economy

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World Development Indicators 2013 59


3 Environment pollution, alone or with sulfur dioxide, creates an enormous burden

urban areas—but also reflects climatic, geographic, and economic

of ill health.

factors. Energy use has been growing rapidly in low- and middle-

Data on particulate matter are estimated average annual concentrations in residential areas away from air pollution “hotspots,” such

income economies, but high-income economies still use almost five times as much energy per capita.

as industrial districts and transport corridors. Data are estimates

Total energy use refers to the use of primary energy before trans-

of annual ambient concentrations of particulate matter in cities of

formation to other end-use fuels (such as electricity and refined

more than 100,000 people by the World Bank’s Agriculture and

petroleum products). It includes energy from combustible renew-

Environmental Services Department.

ables and waste—solid biomass and animal products, gas and

Pollutant concentrations are sensitive to local conditions, and

liquid from biomass, and industrial and municipal waste. Biomass

even monitoring sites in the same city may register different levels.

is any plant matter used directly as fuel or converted into fuel,

Thus these data should be considered only a general indication of

heat, or electricity. Data for combustible renewables and waste

air quality, and comparisons should be made with caution. They

are often based on small surveys or other incomplete informa-

allow for cross-country comparisons of the relative risk of particulate

tion and thus give only a broad impression of developments and

matter pollution facing urban residents. Major sources of urban

are not strictly comparable across countries. The IEA reports

outdoor particulate matter pollution are traffic and industrial emis-

include country notes that explain some of these differences (see

sions, but nonanthropogenic sources such as dust storms may also

Data sources). All forms of energy—primary energy and primary

be a substantial contributor for some cities. Country technology and

­electricity—are converted into oil equivalents. A notional thermal

pollution controls are important determinants of particulate matter.

efficiency of 33 percent is assumed for converting nuclear electric-

Current WHO air quality guidelines are annual mean concentrations

ity into oil equivalents and 100 percent efficiency for converting

of 20 micrograms per cubic meter for particulate matter less than

hydroelectric power.

10 microns in diameter.

Electricity production Carbon dioxide emissions

Use of energy is important in improving people’s standard of liv-

Carbon dioxide emissions are the primary source of greenhouse

ing. But electricity generation also can damage the environment.

gases, which contribute to global warming, threatening human and

Whether such damage occurs depends largely on how electricity

natural habitats. Fossil fuel combustion and cement manufacturing

is generated. For example, burning coal releases twice as much

are the primary sources of anthropogenic carbon dioxide emissions,

carbon dioxide—a major contributor to global warming—as does

which the U.S. Department of Energy’s Carbon Dioxide Information

burning an equivalent amount of natural gas. Nuclear energy does

Analysis Center (CDIAC) calculates using data from the United

not generate carbon dioxide emissions, but it produces other dan-

Nations Statistics Division’s World Energy Data Set and the U.S.

gerous waste products.

Bureau of Mines’s Cement Manufacturing Data Set. Carbon dioxide

The International Energy Agency (IEA) compiles data and data

emissions, often calculated and reported as elemental carbon, were

on energy inputs used to generate electricity. Data for countries

converted to actual carbon dioxide mass by multiplying them by

that are not members of the Organisation for Economic Co-opera-

3.667 (the ratio of the mass of carbon to that of carbon dioxide).

tion and Development (OECD) are based on national energy data

Although estimates of global carbon dioxide emissions are probably

adjusted to conform to annual questionnaires completed by OECD

accurate within 10 percent (as calculated from global average fuel

member governments. In addition, estimates are sometimes made

chemistry and use), country estimates may have larger error bounds.

to complete major aggregates from which key data are missing,

Trends estimated from a consistent time series tend to be more

and adjustments are made to compensate for differences in defini-

accurate than individual values. Each year the CDIAC recalculates

tions. The IEA makes these estimates in consultation with national

the entire time series since 1949, incorporating recent findings and

statistical offices, oil companies, electric utilities, and national

corrections. Estimates exclude fuels supplied to ships and aircraft

energy experts. It occasionally revises its time series to reflect

in international transport because of the difficulty of apportioning

political changes. For example, the IEA has constructed historical

the fuels among benefiting countries.

energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continu-

60 

Energy use

ous changes in coverage or methodology in recent years as more

In developing economies growth in energy use is closely related to

detailed energy accounts have become available. Breaks in series

growth in the modern sectors—industry, motorized transport, and

are therefore unavoidable.

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Environment 3 Definitions

addition to hydropower, coal, oil, gas, and nuclear power generation,

• Deforestation is the permanent conversion of natural forest area

it covers generation by geothermal, solar, wind, and tide and wave

to other uses, including agriculture, ranching, settlements, and

energy as well as that from combustible renewables and waste. Pro-

infrastructure. Deforested areas do not include areas logged but

duction includes the output of electric plants designed to produce

intended for regeneration or areas degraded by fuelwood gathering,

electricity only, as well as that of combined heat and power plants.

acid precipitation, or forest fires. • Nationally protected areas are terrestrial and marine protected areas as a percentage of total ter-

Data sources

ritorial area and include all nationally designated protected areas

Data on deforestation are from FAO (2010) and the FAO’s data

with known location and extent. All overlaps between different desig-

website. Data on protected areas, derived from the UNEP and

nations and categories, buffered points, and polygons are removed,

WCMC online databases, are based on data from national authori-

and all undated protected areas are dated. • Internal renewable

ties, national legislation, and international agreements. Data on

freshwater resources are the average annual flows of rivers and

freshwater resources are from the FAO’s AQUASTAT database. Data

groundwater from rainfall in the country. Natural incoming flows origi-

on access to water and sanitation are from WHO and UNICEF (2012).

nating outside a country’s borders and overlapping water resources

Data on urban population are from the United Nations Population

between surface runoff and groundwater recharge are excluded.

Division (2011). Data on particulate matter concentrations are World

• Access to an improved water source is the percentage of the

Bank estimates. Data on carbon dioxide emissions are from the

population with reasonable access to an adequate amount of water

CDIAC. Data on energy use and electricity production are from IEA

from an improved source, such as piped water into a dwelling, plot,

electronic files and published in IEA’s annual publications, Energy

or yard; public tap or standpipe; tubewell or borehole; protected dug

Statistics of Non-OECD Countries, Energy Balances of Non-OECD

well or spring; and rainwater collection. Unimproved sources include

Countries, Energy Statistics of OECD Countries, and Energy Balances

unprotected dug wells or springs, carts with small tank or drum,

of OECD Countries.

bottled water, and tanker trucks. Reasonable access is defined as the availability of at least 20 liters a person a day from a source

References

within 1 kilometer of the dwelling • Access to improved sanitation

CDIAC (Carbon Dioxide Information Analysis Center). n.d. Online data-

facilities is the percentage of the population with at least adequate

base. http://cdiac.ornl.gov/home.html. Oak Ridge National Labora-

access to excreta disposal facilities (private or shared, but not public) that can effectively prevent human, animal, and insect contact with excreta (facilities do not have to include treatment to render sewage outflows innocuous). Improved facilities range from simple but protected pit latrines to flush toilets with a sewerage connection. To be effective, facilities must be correctly constructed and properly maintained. • Urban population is the midyear population of areas

tory, Environmental Science Division, Oak Ridge, TN. FAO (Food and Agriculture Organization of the United Nations). 2010. Global Forest Resources Assessment 2010. Rome. ———. n.d. AQUASTAT. Online database. www.fao.org/nr/water/ aquastat/data/query/index.html. Rome. IEA (International Energy Agency). Various years. Energy Balances of Non-OECD Countries. Paris.

defined as urban in each country and reported to the United Nations

———.Various years. Energy Balances of OECD Countries. Paris.

divided by the World Bank estimate of total population. • Particulate

———. Various years. Energy Statistics of Non-OECD Countries. Paris.

matter concentration is fine suspended particulates of less than

———.Various years. Energy Statistics of OECD Countries. Paris.

10 microns in diameter (PM10) that are capable of penetrating deep

UNEP (United Nations Environment Programme) and WCMC (World

into the respiratory tract and causing severe health damage. Data

Conservation Monitoring Centre). 2012. Online databases www

are urban-population-weighted PM10 levels in residential areas of

.unep-wcmc-apps.org/species/dbases/about.cfm. Nairobi.

cities with more than 100,000 residents. • Carbon dioxide emis-

United Nations Population Division. 2011. World Urbanization Pros-

sions are emissions from the burning of fossil fuels and the manu-

pects: The 2011 Revision. New York: United Nations, Department of

facture of cement and include carbon dioxide produced during con-

Economic and Social Affairs.

sumption of solid, liquid, and gas fuels and gas flaring. • Energy use

WHO (World Health Organization). 2004. Inheriting the World: The

refers to the use of primary energy before transformation to other

Atlas of Children’s Health and the Environment. www.who.int/ceh/

end use fuels, which equals indigenous production plus imports

publications/atlas/en/index.html. Geneva.

and stock changes, minus exports and fuels supplied to ships and

WHO (World Health Organization) and UNICEF (United Nations Chil-

aircraft engaged in international transport. • Electricity production

dren’s Fund). 2012. Progress on Sanitation and Drinking Water.

is measured at the terminals of all alternator sets in a station. In

Geneva: WHO.

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World Development Indicators 2013 61


3 Environment Online tables and indicators To access the World Development Indicators online tables, use

indicator online, use the URL http://data.worldbank.org/indicator/

the URL http://wdi.worldbank.org/table/ and the table number (for

and the indicator code (for example, http://data.worldbank.org/

example, http://wdi.worldbank.org/table/3.1). To view a specific

indicator/SP.RUR.TOTL.ZS).

3.1 Rural environment and land use Rural population

SP.RUR.TOTL.ZS

Rural population growth

SP.RUR.TOTL.ZG

Land area

AG.LND.TOTL.K2

Forest area

AG.LND.FRST.ZS

Permanent cropland

AG.LND.CROP.ZS

Arable land, % of land area

AG.LND.ARBL.ZS

Arable land, hectares per person

AG.LND.ARBL.HA.PC

3.2 Agricultural inputs Agricultural land, % of land area

AG.LND.AGRI.ZS

Agricultural land, % irrigated

AG.LND.IRIG.AG.ZS

Average annual precipitation

AG.LND.PRCP.MM

Land under cereal production

AG.LND.CREL.HA

Fertilizer consumption, % of fertilizer production

AG.CON.FERT.PT.ZS

Fertilizer consumption, kilograms per hectare of arable land

AG.CON.FERT.ZS

Agricultural employment

SL.AGR.EMPL.ZS

Tractors

AG.LND.TRAC.ZS

ER.H2O.FWAG.ZS

Annual freshwater withdrawals, % for industry

ER.H2O.FWIN.ZS

Annual freshwater withdrawals, % of domestic

ER.H2O.FWDM.ZS

Water productivity, GDP/water use

ER.GDP.FWTL.M3.KD

Access to an improved water source, % of rural population

SH.H2O.SAFE.RU.ZS

Access to an improved water source, % of urban population

SH.H2O.SAFE.UR.ZS

3.6 Energy production and use Energy production Energy use

EG.EGY.PROD.KT.OE EG.USE.COMM.KT.OE ..a,b

Energy use, Average annual growth Energy use, Per capita Fossil fuel

EG.USE.PCAP.KG.OE EG.USE.COMM.FO.ZS

Combustible renewable and waste Alternative and nuclear energy production

EG.USE.CRNW.ZS EG.USE.COMM.CL.ZS

3.7 Electricity production, sources, and access

3.3 Agricultural output and productivity Crop production index

AG.PRD.CROP.XD

Food production index

AG.PRD.FOOD.XD

Livestock production index

AG.PRD.LVSK.XD

Cereal yield

AG.YLD.CREL.KG

Agriculture value added per worker

EA.PRD.AGRI.KD

Electricity production

EG.ELC.PROD.KH

Coal sources

EG.ELC.COAL.ZS

Natural gas sources

EG.ELC.NGAS.ZS

Oil sources

EG.ELC.PETR.ZS

Hydropower sources

EG.ELC.HYRO.ZS

Renewable sources

EG.ELC.RNWX.ZS

3.4 Deforestation and biodiversity

Nuclear power sources

EG.ELC.NUCL.ZS

Forest area

Access to electricity

EG.ELC.ACCS.ZS

AG.LND.FRST.K2 ..a,b

Average annual deforestation Threatened species, Mammals

EN.MAM.THRD.NO

Threatened species, Birds

EN.BIR.THRD.NO

Threatened species, Fishes

EN.FSH.THRD.NO

Threatened species, Higher plants

EN.HPT.THRD.NO

Terrestrial protected areas

ER.LND.PTLD.ZS

Marine protected areas

ER.MRN.PTMR.ZS

3.5 Freshwater

62 

Annual freshwater withdrawals, % for agriculture

Internal renewable freshwater resources

ER.H2O.INTR.K3

Internal renewable freshwater resources, Per capita

ER.H2O.INTR.PC

Annual freshwater withdrawals, cu. m

ER.H2O.FWTL.K3

Annual freshwater withdrawals, % of internal resources

ER.H2O.FWTL.ZS

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3.8 Energy dependency, efficiency and carbon dioxide emissions Net energy imports GDP per unit of energy use

EG.IMP.CONS.ZS EG.GDP.PUSE.KO.PP.KD

Carbon dioxide emissions, Total Carbon dioxide emissions, Carbon intensity

EN.ATM.CO2E.KT EN.ATM.CO2E.EG.ZS

Carbon dioxide emissions, Per capita Carbon dioxide emissions, kilograms per 2005 PPP $ of GDP

EN.ATM.CO2E.PC EN.ATM.CO2E.PP.GD.KD

3.9 Trends in greenhouse gas emissions Carbon dioxide emissions, Average annual growth

..a,b

Carbon dioxide emissions, % change

..a,b

Methane emissions, Total

EN.ATM.METH.KT.CE

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Environment 3 ..a,b

Methane emissions, % change Methane emissions, From energy processes Methane emissions, Agricultural Nitrous oxide emissions, Total

EN.ATM.METH.EG.ZS

Population in urban agglomerations of more than 1 million

EN.URB.MCTY.TL.ZS

EN.ATM.METH.AG.ZS

Population in the largest city

EN.URB.LCTY.UR.ZS

EN.ATM.NOXE.KT.CE ..a,b

Nitrous oxide emissions, % change Nitrous oxide emissions, Energy and industry

EN.ATM.NOXE.EI.ZS

Nitrous oxide emissions, Agriculture

EN.ATM.NOXE.AG.ZS

Other greenhouse gas emissions, Total

EN.ATM.GHGO.KT.CE ..a,b

Other greenhouse gas emissions, % change

3.10 Carbon dioxide emissions by sector Electricity and heat production

EN.CO2.ETOT.ZS

Manufacturing industries and construction

EN.CO2.MANF.ZS

Residential buildings and commercial and public services

EN.CO2.BLDG.ZS

Transport

EN.CO2.TRAN.ZS

Other sectors

EN.CO2.OTHX.ZS

Access to improved sanitation facilities, % of urban population

SH.STA.ACSN.UR

Access to improved sanitation facilities, % of rural population

SH.STA.ACSN.RU

3.13 Traffic and congestion Motor vehicles, Per 1,000 people

IS.VEH.NVEH.P3

Motor vehicles, Per kilometer of road

IS.VEH.ROAD.K1

Passenger cars

IS.VEH.PCAR.P3

Road density

IS.ROD.DNST.K2

Road sector energy consumption, % of total consumption

IS.ROD.ENGY.ZS

Road sector energy consumption, Per capita

IS.ROD.ENGY.PC

Diesel fuel consumption

IS.ROD.DESL.PC

Gasoline fuel consumption

IS.ROD.SGAS.PC

Pump price for super grade gasoline

EP.PMP.SGAS.CD

resilience

Pump price for diesel

EP.PMP.DESL.CD

Average daily minimum/maximum temperature

..b

Projected annual temperature

..b

Urban-population-weighted particulate matter concentrations (PM10)

Projected annual cool days/cold nights

..b

3.14 Air pollution

Projected annual hot days/warm nights

..b

Projected annual precipitation

..b

This table provides air pollution data for major cities.

3.11 Climate variability, exposure to impact, and

EN.ATM.PM10.MC.M3

..b

Land area with an elevation of 5 meters or less

AG.LND.EL5M.ZS

Population living in areas with elevation of 5 meters or less

EN.POP.EL5M.ZS

Population affected by droughts, floods, and extreme temperatures

Total natural resources rents

NY.GDP.TOTL.RT.ZS

EN.CLC.MDAT.ZS

Oil rents

NY.GDP.PETR.RT.ZS

Disaster risk reduction progress score

EN.CLC.DRSK.XQ

Natural gas rents

NY.GDP.NGAS.RT.ZS

Coal rents

NY.GDP.COAL.RT.ZS

Mineral rents

NY.GDP.MINR.RT.ZS

Forest rents

NY.GDP.FRST.RT.ZS

3.12 Urbanization Urban population

SP.URB.TOTL

Urban population, % of total population Urban population, Average annual growth

Economy

3.15 Contribution of natural resources to gross domestic product

SP.URB.TOTL.IN.ZS SP.URB.GROW

States and markets

a. Derived from data elsewhere in the World Development Indicators database. b. Available online only as part of the table, not as an individual indicator.

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World Development Indicators 2013 63


ECONOMY 64â&#x20AC;&#x192;

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The data in the Economy section provide a picture of the global economy and the economic activity of more than 200 countries and territories that produce, trade, and consume the world’s output. They include measures of macroeconomic performance and stability and broader measures of income and savings adjusted for pollution, depreciation, and depletion of resources. The world economy grew 2.3 percent in 2012, to reach $71 trillion, and the share from developing economies grew to 34.3 percent. Growth is expected to remain around 2.4 percent in 2013. Low- and middle-income economies, estimated to have grown 5.1 percent in 2012, are projected to expand 5.5 percent in 2013. Growth in highincome economies has been downgraded from earlier forecasts to 1.3  percent in 2012 and 2013. Beginning in August 2012, the International Monetary Fund implemented the Balance of Payments Manual 6 (BPM6) framework in its major statistical publications. The World Bank will implement BPM6 in its online databases and publications in April 2013. Balance of payments data for 2005 onward will be presented in accord with the BPM6. The historical BPM5 data series will end with data for 2008, which can be accessed through the World Development Indicators archives. The change to the BPM6 framework will affect some components of the balance of payments. In the current account, “merchanting”—the purchase of goods from a nonresident and the subsequent resale to another nonresident without

Economy

States and markets

the goods being present in the economy—has been reclassified from services to goods, while manufacturing services performed on physical inputs owned by others along with maintenance and repair services were reclassified from goods to services. In the capital account, reverse investment in direct investment has been reclassified to present assets and liabilities on a gross basis in the balance of payments and international investment position. No changes were made to the balances on current account, capital account, or financial account. Levels of reserves were not adjusted, nor were net errors and omissions. For many economies changes to major aggregates and balancing items will be limited. The change in the methodology for “goods for processing” results in increases in imports and exports of services (equivalent to the amounts received or paid for manufacturing services) and larger reductions in gross imports and exports of goods (due to the elimination of imputed transactions in goods that do not change ownership), though net goods and services trade may not be affected. The change in the recording of reverse investment in foreign direct investment will result in substantial increases in both international investment position assets and liabilities for many economies under BPM6, though net international investment position is not affected by this change. The complete balance of payments methodology can be accessed through the International Monetary Fund website (www.imf.org/external/ np/sta/bop/bop.htm).

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World Development Indicators 2013 65


Highlights East Asia & Pacific: Service sector has potential for more growth The service sector contributed substantially to gross domestic product

Value added in services as a share of GDP, 2010 (%)

(GDP) growth in many East Asia and Pacific economies in recent years,

100

constituting nearly half of GDP and contributing 3.7 percentage points to an overall growth rate of 8.5 percent. Reflecting strong domestic

75

Larger than expected

Kiribati

Fiji

demand, continuing growth of services is consistent with long-term

Samoa

trends of rising incomes in other regions. But despite recent growth,

Tonga

50 Cambodia

Vietnam

Lao PDR

the service sector in many East Asia and Pacific economies is smaller

Philippines China

Mongolia

Indonesia

Malaysia

Smaller than expected

Thailand

than expected based on average income. This reflects the relative success of manufacturing among the countries in the region. It may also be the result of limited adoption of high-value, modern services

25 Papua New Guinea

(information and communications technology, finance and professional business services). Few East Asia and Pacific countries besides the

0 500

5,000

Philippines have developed robust industries focused on exporting

50,000

GDP per capita, 2010 (2005 PPP $, log scale)

modern services (World Bank 2012a; Asian Development Bank 2012).

Source: World Development Indicators database.

Europe & Central Asia: Volatile food and energy prices pose a challenge Europe and Central Asia is in general a net exporter of energy, but many

Net energy exports or imports, 2010 (% of energy use)

economies in the region depend heavily on energy imports and are Azerbaijan Turkmenistan Kazakhstan Russian Federation Uzbekistan Montenegro Romania Albania Kosovo Bosnia & Herzegovina Serbia Poland Tajikistan Bulgaria Ukraine Macedonia, FYR Croatia Georgia Kyrgyz Rep. Armenia Turkey Belarus Moldova

thus vulnerable to sudden price changes. Azerbaijan, Kazakhstan, the Net energy exporters Net energy importers

Russian Federation, Turkmenistan, and Uzbekistan, the main exporters, stand to benefit from rising world energy prices. But net energy imports account for 96 percent of energy use in Moldova, 84 percent in Belarus, 69 percent in Turkey, 64 percent in Armenia, and 59 percent in the Kyrgyz Republic. Most energy imports are oil, but Belarus, Croatia, Poland, and Serbia are also large importers of electricity. Some of these economies may face an acute deterioration of their balance of payments positions if oil prices rise. And some are also vulnerable to rising food prices, triggered by the substitution of cropbased fuels for petroleum-based fuels.

–500

–250

0

250

Source: Online table 3.9.

Latin America & Caribbean: Tracking global uncertainties GDP growth in Latin America and the Caribbean fell 1.7 percentage

GDP growth (%)

points from 2011 to 3.0 percent in 2012, the second largest drop

10

among developing country regions after Europe and Central Asia, where growth fell 2.8 percentage points. The region’s GDP growth decelerated Chile

5

due to slowing domestic demand and a weak external environment. The slowdown was particularly severe in Brazil, the region’s largest econ-

Latin America & Caribbean

0

Brazil

omy, where global uncertainties and earlier fiscal, monetary, and credit policy tightening to contain inflation risks had a large impact, especially on private investment. In Chile growth remained buoyant and continued

Mexico

to expand briskly, if slightly slower than in 2011. Growth in Central

–5

America and the Caribbean slowed modestly, while growth in Mexico (the second largest economy in the region) rose slightly in 2012, to 4

–10

percent, benefiting from the fairly strong recovery in U.S. manufacturing 2007

2008

2009

2010

2011

2012

(De la Torre, Didier, and Pienknagura 2012; World Bank 2013).

Source: Online table 4.1.

66 

World Development Indicators 2013

Front

?

User guide

World view

People

Environment


Middle East & North Africa: Recovery has been slow Macroeconomic fundamentals weakened in most Middle East and North Africa countries in 2011 and 2012, as growth slowed and governments responded to social pressures with expansionary fis-

GDP growth (%) 10

Lebanon

cal policies. High oil prices heightened current account and fiscal deficits in oil importers, especially in places where governments

Morocco

5

subsidize energy use. Domestic pressures coupled with a challenging global environment and spillovers from regional events also

Egypt, Arab Rep.

0

Algeria

weighed heavily on the economies of some oil importers such as Jordan, Lebanon, and Morocco. Postrevolutionary economies such

Jordan

Tunisia

–5

as the Arab Republic of Egypt, Tunisia, and the Republic of Yemen are recovering after the Arab Spring turmoil. However, recovery has

Yemen, Rep.

–10

taken place in a weak global environment. The transition in these countries is far from complete, and uncertainty around the reform pro-

–15

cess continues to constrain private investment (World Bank 2012b).

2007

2008

2009

2010

2011

2012

Source: Online table 4.1.

South Asia: Revenues are low and stagnating South Asian countries collect exceptionally low levels of tax revenue. Revenue collection by central governments in the region averages 10–15 percent of GDP, compared with 20 percent in similar develop-

Tax revenue (% of GDP) 20

ing economies and higher rates in more developed economies. In most South Asian countries the major source of revenue is the value added

15

Sri Lanka

tax. Some are changing their tax structures—India is adopting a goods and services tax, for example—but the main problem remains low collection rates for the value added tax and income tax. Most South

Nepal India

Maldives

Pakistan

10

Asian countries have outdated tax laws and inadequate institutional arrangements unsuitable to the growing complexity of their economies. South Asian countries all need to upgrade their infrastructure and

5

Bhutan

Bangladesh

Afghanistan

improve social services such as education and health care, which will require a substantial increase in fiscal spending and better sources

0 2000

of revenue (World Bank 2012c).

2002

2004

2006

2008

2010 2011

Source: Online table 5.6.

Sub-­Saharan Africa: Resilient growth in an uncertain global economic environment Despite a sluggish global economy, economic conditions in Sub-­ Saharan Africa held up well in 2011–12. Robust domestic demand, high commodity prices, rising export volumes (due to new capacity in the natural resources sector), and steady remittance flows supported

GDP growth (%) 25 Angola 20

growth in 2012. GDP in Sub-­Saharan Africa expanded at an average of 4.9 percent a year over 2000–11 and rose an estimated 4.6 percent

15

in 2012, the third most among developing regions. Excluding South Africa, the region’s largest economy, GDP output rose 5.8 percent in 2012, with a third of countries growing at least 6 percent. Growth varied across the region, with steady expansion in most low-income countries but slow growth in middle-income countries, such as South Africa, that are more tightly integrated with the global economy and in some countries affected by political instability, such as Mali (IMF 2012; World Bank 2013).

10

Nigeria

5 0 –5

Sub-Saharan Africa South Africa 2007

2008

Mali

2009

2010

2011

2012

Source: Online table 4.1.

Economy

States and markets

Global links

Back

World Development Indicators 2013 67


4 Economy Gross domestic product

Gross savings

Adjusted net savings

Current account balance

Central Central Consumer government government price index cash surplus debt or deficit

Broad money

average annual % growth 2000–11

Afghanistan

Forecast

% of GDP

% of GNI

% of GDP

% of GDP

% of GDP

% growth

% of GDP

2012–13

2011

2011

2011

2011

2011

2011

2011

..

..

..

..

..

..

–0.6

..

5.7

Albania

5.2

0.8

1.6

12.7

0.9

–12.1

..

..

3.5

81.8

Algeria

3.7

3.0

3.4

48.4

25.3

10.4

–0.3

..

4.5

64.6 ..

American Samoa Andorra Angola Antigua and Barbuda Argentina Armenia Aruba Australia Austriaa

35.8

..

..

..

..

..

..

..

..

..

5.9

..

..

..

..

..

..

..

..

..

12.2

8.1

7.2

20.2

–21.4

12.5

..

..

13.5

35.9

2.7

1.7

2.4

26.8

..

–10.7

..

..

3.5

101.4

..

..

..

22.3

11.3

0.0

..

..

..

28.8

8.2

6.8

4.3

19.3

8.7

–10.8

–2.7

..

7.7

29.5

..

..

..

..

..

..

..

..

4.4

..

3.1

..

..

22.9

6.8

–2.8

–3.7

30.7

3.4

105.8

1.8

..

..

25.5

16.4

0.5

–2.2

75.0

3.3

..

16.0

2.0

4.2

45.9

7.5

27.0

1.4

6.4

7.9

27.8

Bahamas, The

0.5

..

..

11.5

..

–14.0

–3.5

..

3.2

80.5

Bahrain

6.3

..

..

30.1

9.6

3.4

..

..

–0.4

91.2

Bangladesh

6.0

6.1

6.0

36.5

25.0

0.2

–0.9

..

10.7

68.7

Barbados

0.9

..

..

16.9

..

–5.3

–8.6

104.4

9.4

152.3

Belarus

7.8

2.8

4.0

26.3

17.6

–10.5

1.9

44.2

53.2

40.5

Belgiuma

1.5

..

..

22.3

14.1

–1.4

–3.6

91.0

3.5

..

Belize

3.7

4.0

2.8

..

..

–2.2

–1.6

..

–2.5

76.0

Benin

3.8

3.5

3.8

13.1

7.7

–8.1

–1.4

..

2.7

40.0

Bermuda

1.9

..

..

..

..

..

..

..

..

..

Bhutan

8.5

..

..

..

..

..

0.5

56.8

8.8

67.1

Bolivia

4.2

4.7

4.4

26.1

6.9

2.2

..

..

9.8

68.7

Bosnia and Herzegovina

4.2

..

..

13.1

..

–8.8

–1.2

..

3.7

56.7

Botswana

4.0

5.8

5.1

27.7

20.4

0.3

–1.7

..

8.9

37.8

Brazil

3.8

0.9

3.4

17.2

6.8

–2.1

–2.6

52.8

6.6

74.4

Brunei Darussalam

1.2

..

..

50.9

10.1

37.1

..

..

2.0

67.2

Bulgaria

4.3

0.8

1.8

23.6

13.7

0.4

–2.0

15.5

4.2

75.6

Burkina Faso

5.8

6.4

6.7

15.6

5.5

–4.6

–2.4

..

2.8

29.0

Burundi

3.5

4.1

4.3

1.7

–6.1

–12.2

..

..

9.7

24.2

Cambodia

8.4

6.6

6.7

10.6

3.4

–5.5

–4.2

..

5.5

39.1

Cameroon

3.2

4.6

4.8

12.2

0.8

–3.8

..

..

2.9

23.1

Azerbaijan

Canada

1.9

..

..

19.6

6.7

–2.8

–1.3

53.8

2.9

..

Cape Verde

6.2

4.8

4.9

22.2

16.7

–16.0

–3.7

..

4.5

77.0

Cayman Islands

..

..

..

..

..

..

..

..

..

..

Central African Republic

1.3

3.8

4.0

..

..

..

..

..

1.3

19.2

Chad

8.7

..

..

..

..

..

..

..

–4.9

13.8

Channel Islands

0.5

..

..

..

..

..

..

..

..

..

Chile

4.1

5.8

5.1

24.9

5.4

1.8

1.3

..

3.3

76.1

10.8

7.9

8.4

52.7

36.4

2.8

..

..

5.4

180.1

4.6

..

..

29.4

18.1

5.2

4.1

35.5

5.3

328.2 101.9

China Hong Kong SAR, China Macao SAR, China

68 

Estimate 2011–12

12.5

..

..

55.9

..

42.7

25.0

..

5.8

Colombia

4.5

3.5

3.8

19.1

1.3

–3.0

0.3

53.2

3.4

39.9

Comoros

1.9

2.5

3.5

..

..

..

..

..

0.9

34.9

Congo, Dem. Rep.

5.6

6.6

8.2

..

..

..

3.8

..

..

16.8

Congo, Rep.

4.5

4.7

5.6

..

..

..

..

..

1.3

27.0

World Development Indicators 2013

Front

?

User guide

World view

People

Environment


Economy 4 Gross domestic product

Gross savings

Adjusted net savings

Current account balance

Central Central Consumer government government price index cash surplus debt or deficit

Broad money

average annual % growth Estimate

Forecast

% of GDP

% of GNI

% of GDP

% of GDP

% of GDP

% growth

% of GDP

2000–11

2011–12

2012–13

2011

2011

2011

2011

2011

2011

2011

Costa Rica

4.8

4.6

4.0

14.7

9.0

–5.3

–3.5

..

4.9

49.8

Côte d’Ivoire

1.0

8.2

7.0

12.7

4.1

2.0

..

..

4.9

40.5

Croatia

2.6

..

..

19.9

10.7

–0.7

–4.6

..

2.3

72.9

Cuba

6.1

..

..

..

..

..

..

..

..

..

Curaçao

..

..

..

..

..

..

..

..

..

..

Cyprusa

2.9b

..

..

8.8b

4.6 b

–4.7

–6.3

114.0

3.3

..

Czech Republic

3.7

..

..

21.7

12.6

–2.9

–4.4

38.1

1.9

73.9

Denmark

0.8

..

..

23.5

15.0

5.7

–2.0

50.6

2.8

74.0

Djibouti

4.0

..

..

..

..

–6.8

..

..

4.4

89.7

Dominica

3.5

0.4

1.2

5.0

–3.7

–17.5

..

..

2.4

86.7

Dominican Republic

5.7

3.0

4.3

8.4

–1.7

–8.1

–2.9

..

8.5

33.4

Ecuador

4.8

4.5

3.9

22.8

–3.2

–0.4

..

..

4.5

35.6

Egypt, Arab Rep.

5.0

2.4

3.2

16.9

1.4

–2.4

–10.1

..

10.1

76.1

El Salvador

2.0

1.8

2.3

8.9

0.7

–4.6

–2.2

48.0

5.1

45.7

Equatorial Guinea Eritrea

14.4

..

..

..

..

..

..

..

6.9

11.5

0.9

7.5

6.0

..

..

..

..

..

..

114.7

Estoniaa

3.9

..

..

25.1

16.3

2.2

1.0

7.1

5.0

59.8

Ethiopia

8.9

7.8

7.5

27.3

17.6

–2.6

–1.4

..

33.2

..

..

..

..

..

..

–0.7

..

..

..

..

1.2

..

..

..

..

–13.0

..

..

8.7

66.3

Finlanda

1.9

..

..

19.7

11.5

–1.2

–0.5

47.8

3.4

..

Francea

1.2

..

..

17.9

9.0

–2.0

–5.2

93.9

2.1

..

..

..

..

..

..

..

..

..

..

..

2.4

4.7

3.5

..

..

..

..

..

1.3

22.1

10.7

Faeroe Islands Fiji

French Polynesia Gabon Gambia, The

3.4

3.9

Georgia

6.6c

4.9c

12.1

6.6

5.1c

13.9c

4.9c

Germanya

1.1

Ghana

..

..

23.9

6.3

7.5

7.8

Greecea

1.8

..

Greenland

1.7

..

Grenada

2.2

..

Guam Guatemala

7.5

..

..

4.8

54.9

–11.8

–1.2

32.6

49.4

29.3

13.8

5.6

–0.4

55.6

2.1

..

8.8

–6.6

–8.9

–4.0

..

8.7

30.8

..

5.4

–5.5

–9.9

–9.8

106.5

3.3

..

..

..

..

..

..

..

..

..

..

–5.7

..

–26.6

–3.0

..

3.0

94.3

..

..

..

..

..

..

..

..

..

..

3.6

3.1

3.2

10.7

0.9

–3.1

–2.8

24.6

6.2

45.5

Guinea

2.6

4.8

5.0

–6.5

–27.7

–22.8

..

..

21.4

36.4

Guinea-Bissau

2.5

–2.8

3.0

..

..

–8.5

..

..

5.0

40.5 65.8

Guyana

2.5

..

..

14.5

–1.3

–7.1

..

..

5.0

Haiti

0.7

2.2

6.0

24.6

17.0

–4.6

..

..

8.4

47.2

Honduras

4.4

3.3

3.7

17.9

11.0

–8.6

–2.6

..

6.8

52.2

Hungary

1.9

..

..

20.6

13.0

0.9

3.6

80.9

4.0

63.7

Iceland

2.7

..

..

7.3

0.9

–6.9

–5.3

119.3

4.0

96.3

India

7.8

5.5

5.9

34.6

22.5

–3.0

–3.7

48.5

8.9

76.7

Indonesia

5.4

6.1

6.3

31.8

17.1

0.2

–1.1

26.2

5.4

38.8

Iran, Islamic Rep.

5.4

..

..

..

..

..

0.5

..

20.6

45.0

Iraq

1.1

..

..

..

..

22.7

..

..

2.9

54.9

Irelanda

2.4

..

..

14.0

5.5

1.2

–13.5

106.0

2.6

..

Isle of Man

6.2

..

..

..

..

..

..

..

..

..

Israel

3.6

..

..

14.7

6.3

0.1

–4.4

..

3.5

104.5

Economy

States and markets

Global links

Back

World Development Indicators 2013 69


4 Economy Gross domestic product

Gross savings

Adjusted net savings

Current account balance

Central Central Consumer government government price index cash surplus debt or deficit

Broad money

average annual % growth 2000–11

Italya Jamaica

Forecast

% of GDP

% of GNI

% of GDP

% of GDP

% of GDP

% growth

% of GDP

2012–13

2011

2011

2011

2011

2011

2011

2011

0.4

..

..

16.4

6.8

–3.1

–3.5

110.8

2.7

..

..

..

..

8.4

1.3

–14.3

–5.1

..

7.5

50.8

Japan

0.7

..

..

21.7

10.4

2.0

–8.3

175.0

–0.3

239.2

Jordan

6.5

3.0

3.3

13.4

5.8

–12.0

–6.8

61.9

4.4

129.6

Kazakhstan

8.0

5.0

5.5

29.7

–4.3

7.5

7.7

9.9

8.3

35.4

Kenya

4.4

4.3

4.9

13.5

11.3

–9.9

–4.6

..

14.0

51.0

Kiribati

0.4

..

..

..

..

..

..

..

..

..

..

..

..

..

..

..

..

..

..

..

Korea, Rep.

4.0

..

..

31.5

22.2

2.4

1.8

..

4.0

78.1

Kosovo

5.2

..

..

..

..

..

..

..

7.3

41.0

Kuwait

6.0

..

..

48.7

12.2

29.6

25.0

..

4.7

56.9

Kyrgyz Republic

4.4

1.0

8.5

18.2

5.6

–6.1

–4.8

..

16.5

..

Lao PDR

7.3

8.2

7.5

16.3

–1.7

–2.5

–0.9

..

7.6

35.9

Latvia

4.1

5.3

3.0

26.1

18.5

–2.2

–2.9

42.5

4.4

47.0

Lebanon

5.0

1.7

2.8

11.5

0.8

–12.1

–5.9

..

4.0

242.0

Lesotho

3.8

4.3

5.2

15.1

..

–21.4

..

..

5.0

37.9

Liberia

6.0

..

..

30.4

23.0

–48.9

0.0

..

8.5

38.2

Libya

5.4

..

..

..

..

15.0

..

..

2.5

58.0

Liechtenstein

2.5

..

..

..

..

..

..

..

..

..

Lithuania

4.7

3.3

2.5

17.5

9.6

–1.4

–5.2

43.7

4.1

47.6

Korea, Dem. Rep.

Luxembourga

2.9

..

..

22.0

12.4

7.1

–0.4

16.8

3.4

..

Macedonia, FYR

3.3

0.0

1.0

25.6

14.9

–3.0

..

..

3.9

55.7

Madagascar

3.2

2.2

4.5

..

..

..

..

..

9.5

24.9

Malawi

5.1

4.1

5.4

13.5

9.8

–13.6

..

..

7.6

35.7

Malaysia

4.9

5.1

5.0

34.6

20.6

11.0

–4.8

51.8

3.2

138.5

Maldives

7.2

..

..

..

..

–23.9

–16.7

68.4

12.8

63.7

Mali

5.1

–1.5

3.5

8.5

–5.2

–12.6

–2.5

..

2.9

29.1

Maltaa

1.8

..

..

9.5

..

–0.2

–2.8

86.5

2.7

..

Marshall Islands

1.5

..

..

..

..

..

..

..

..

..

Mauritania

5.6

4.8

5.2

..

..

..

..

..

5.7

32.8

Mauritius

3.9

3.3

3.6

13.8

4.8

–12.6

–1.1

36.3

6.5

103.3

Mexico

2.1

4.0

3.3

26.6

12.4

–1.0

..

..

3.4

31.4

Micronesia, Fed. Sts.

0.0

..

..

..

..

..

..

..

..

38.2

Moldova

5.1d

1.0 d

3.0 d

–11.3

–1.8

23.8

7.7

49.9

Monaco

4.3

..

..

..

..

..

..

..

..

..

Mongolia

7.4

11.8

16.2

31.1

–5.5

–31.5

–3.1

46.9

9.5

57.8

13.0 d

10.2d

Montenegro

4.2

..

..

..

..

..

3.2

47.4

Morocco

4.8e

3.0e

4.4 e

28.1e

20.2e

–8.0

–4.1

56.3

0.9

112.4

Mozambique

7.5

7.5

8.0

12.4

5.6

–19.1

..

..

10.4

38.8

..

..

..

..

..

..

..

..

5.0

..

4.9

4.2

4.3

18.6

14.6

–1.2

..

..

5.0

66.6

Nepal

3.9

4.2

4.0

34.2

27.9

1.5

–1.0

33.8

9.5

75.7

Netherlandsa

1.5

..

..

26.1

15.1

9.7

–3.9

66.0

2.4

..

..

..

..

..

..

..

..

..

..

..

New Zealand

2.0

..

..

18.8

11.2

–4.2

–7.3

63.6

4.4

95.8

Nicaragua

3.2

4.0

4.2

19.0

11.2

–14.0

0.5

..

8.1

34.5

Niger

4.2

12.0

6.8

..

..

–25.1

..

..

2.9

21.4

Myanmar Namibia

New Caledonia

70 

Estimate 2011–12

World Development Indicators 2013

Front

..

?

..

User guide

World view

People

Environment


Economy 4 Gross domestic product

Gross savings

Adjusted net savings

Current account balance

Central Central Consumer government government price index cash surplus debt or deficit

Broad money

average annual % growth Estimate

Forecast

% of GDP

% of GNI

% of GDP

% of GDP

% of GDP

% growth

% of GDP

2000–11

2011–12

2012–13

2011

2011

2011

2011

2011

2011

2011

6.8

6.5

6.6

..

..

3.6

..

..

10.8

33.6

..

..

..

..

..

..

..

..

..

..

Norway

1.6

..

..

37.7

19.2

14.5

14.7

20.4

1.3

..

Oman

4.9

..

..

..

..

14.3

–0.8

5.1

4.1

35.7

Pakistan

4.9

3.8

3.9

20.4

9.3

–1.1

–6.5

..

11.9

38.0

Palau

0.3

..

..

..

..

..

..

..

..

..

Panama

7.2

10.0

7.5

17.7

9.4

–14.5

..

..

5.9

98.1

Papua New Guinea

4.3

8.0

4.0

20.3

..

–6.7

..

..

8.4

49.9

Paraguay

4.1

–1.0

8.5

10.6

3.6

–1.1

1.1

..

8.3

45.0

Peru

6.2

6.3

5.8

23.4

5.4

–1.9

1.3

19.5

3.4

35.8

Philippines

4.9

6.0

6.2

25.1

14.6

3.1

–1.8

..

4.6

59.8

Poland

4.3

..

..

16.9

9.2

–4.9

–4.3

..

4.2

58.0

Portugala

0.5

..

..

11.7

–1.6

–6.5

–4.0

92.6

3.7

..

Puerto Rico

0.0

..

..

..

..

..

..

..

..

..

13.6

..

..

..

..

..

2.9

..

1.9

49.2

Nigeria Northern Mariana Islands

Qatar Romania

4.4

0.6

1.6

24.8

14.4

–4.4

–4.9

..

5.8

37.3

Russian Federation

5.1

3.5

3.6

30.3

7.2

5.3

3.4

9.5

8.4

52.7

Rwanda

7.9

7.7

7.5

11.3

4.5

–7.5

..

..

5.7

..

Samoa

2.6

..

..

..

..

–11.9

..

..

5.2

47.2

San Marino

3.2

..

..

..

..

..

..

..

2.6

..

..

..

..

..

..

–43.5

..

..

11.9

35.8

São Tomé and Príncipe Saudi Arabia

3.7

..

..

46.8

3.8

27.5

..

..

5.0

57.2

Senegal

4.1

3.7

4.8

21.8

17.3

–4.7

..

..

3.4

40.2 44.7

Serbia

3.7

..

..

16.4

..

–8.4

–4.2

..

11.1

Seychelles

2.9

3.3

4.2

..

..

–21.4

5.6

75.3

2.6

57.9

Sierra Leone

6.6

25.0

11.1

9.8

–6.0

–37.9

–4.6

..

16.2

21.6

Singapore

6.0

..

..

46.6

35.7

23.3

9.8

112.7

5.3

135.7

..

..

..

..

..

..

..

..

..

..

Slovak Republica

5.1

..

..

16.5

7.2

0.0

–4.9

45.6

3.9

..

Sloveniaa

2.9

..

..

21.4

13.2

0.0

–6.0

..

1.8

..

Solomon Islands

4.9

5.3

4.0

..

..

–30.1

..

..

7.3

40.8

Sint Maarten

Somalia South Africa South Sudan

..

..

..

..

..

..

..

..

..

..

3.7

2.4

2.7

16.4

1.5

–3.4

–4.4

..

5.0

76.1 ..

..

–2.0

11.0

..

..

..

..

..

..

Spaina

2.1

..

..

18.2

9.1

–3.5

–3.5

55.2

3.2

..

Sri Lanka

5.8

6.1

6.8

22.1

13.4

–7.8

–6.4

..

6.7

38.1

St. Kitts and Nevis

2.5

..

..

23.4

..

–8.6

2.6

..

5.9

138.3

St. Lucia

2.9

0.7

1.2

11.8

..

–22.5

..

..

2.8

91.3

..

..

..

..

..

..

..

..

..

..

St. Vincent and Grenadines

3.1

1.2

1.5

–4.8

–12.2

–30.2

–3.7

..

4.0

67.8

Sudan

7.1f

3.0 f

3.2f

20.9 f

0.1f

0.2

..

..

13.0

24.5

Suriname

4.9

4.0

4.5

..

..

5.8

..

..

17.7

47.5

Swaziland

2.4

–2.0

1.0

–0.5

–5.1

–10.0

..

..

6.1

29.6

Sweden

2.3

..

..

26.2

18.7

6.5

0.5

38.2

3.0

86.6

Switzerland

1.9

..

..

32.1

22.0

7.5

..

..

0.2

167.8

Syrian Arab Republic

5.0

..

..

16.8

–5.7

–0.6

..

..

4.8

73.9

Tajikistan

8.0

..

..

23.2

16.4

–12.1

..

..

12.4

..

St. Martin

Economy

States and markets

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World Development Indicators 2013 71


4 Economy Gross domestic product

Gross savings

Adjusted net savings

Current account balance

Central Central Consumer government government price index cash surplus debt or deficit

Broad money

average annual % growth Estimate

Forecast

% of GDP

% of GNI

% of GDP

% of GDP

% of GDP

% growth

% of GDP

2000–11

2011–12

2012–13

2011

2011

2011

2011

2011

2011

2011

Tanzaniag

7.0

6.5

6.8

20.3

10.5

–16.6

..

..

12.7

34.7

Thailand

4.2

4.7

5.0

31.0

20.8

4.1

–1.2

30.2

3.8

128.2

Timor-Leste

5.6

..

..

..

..

..

..

..

13.5

30.6

Togo

2.6

4.0

4.4

12.2

5.3

–6.3

–1.1

..

3.6

48.5

Tonga

1.0

..

..

3.9

..

–17.0

..

..

6.3

39.0

Trinidad and Tobago

5.6

..

..

..

..

19.9

–4.8

21.3

5.1

65.1

Tunisia

4.4

2.4

3.2

15.9

5.8

–7.3

–3.7

44.0

3.6

67.6

Turkey

4.7

2.9

4.0

14.1

4.0

–10.0

–1.3

45.9

6.5

54.7

Turkmenistan

8.7

..

..

..

..

..

..

..

..

..

..

..

..

..

..

..

..

..

..

..

Tuvalu

1.1

..

..

..

..

..

..

..

..

..

Uganda

7.7

3.4

6.2

20.8

11.2

–13.5

–3.9

42.7

18.7

26.5

Ukraine

4.3

0.5

2.2

16.0

6.2

–5.5

–2.3

27.1

8.0

52.1

United Arab Emirates

4.8

..

..

..

..

..

..

..

0.9

62.4

United Kingdom

1.7

..

..

12.9

2.7

–1.9

–7.7

101.2

4.5

165.7

United States

1.6

..

..

11.7

0.9

–3.2

–9.3

81.8

3.2

89.8

Uruguay

4.0

4.0

4.0

16.5

5.9

–3.1

–0.6

46.6

8.1

44.6

Uzbekistan

7.3

8.0

6.5

..

..

..

..

..

..

..

Vanuatu

3.9

2.0

2.5

..

..

–15.2

..

..

0.9

84.2

Venezuela, RB

4.4

5.2

1.8

30.7

1.4

8.6

..

..

26.1

36.6

Vietnam

7.3

5.2

5.5

33.1

17.4

0.2

..

..

18.7

109.3

Virgin Islands (U.S.)

..

..

..

..

..

..

..

..

..

..

West Bank and Gaza

..

..

..

..

..

..

..

..

2.8

..

3.6

0.1

4.0

8.3

–10.0

–3.0

..

..

16.4

30.1

Turks and Caicos Islands

Yemen, Rep. Zambia Zimbabwe World

5.7

6.7

7.1

27.8

4.0

1.1

–1.5

..

6.4

23.4

–5.1

5.0

6.0

..

..

..

..

..

..

..

2.3 w

2.4 w

2.7 w

19.4 w

6.7 w

Low income

5.5

5.6

6.1

26.5

Middle income

6.4

4.9

5.5

30.0

15.1

Lower middle income

6.2

4.8

5.5

28.1

13.4 15.6

15.6

Upper middle income

6.5

5.0

5.5

31.0

Low & middle income

6.4

4.9

5.5

29.9

15.1

East Asia & Pacific

9.4

7.5

7.9

47.7

30.3

Europe & Central Asia

5.2

3.0

3.6

22.9

6.2

Latin America & Carib.

3.9

3.0

3.6

21.5

8.6

Middle East & N. Africa

4.7

0.2

2.4

..

..

South Asia

7.3

5.4

5.7

33.3

21.2

Sub-­Saharan Africa

–2.4

4.9

4.6

4.9

16.8

High income

1.6

1.3

1.3

17.6

5.7

Euro area

1.2

–0.4

–0.1

20.1

8.6

a. As members of the European Monetary Union, these countries share a single currency, the euro. b. Refers to the area controlled by the government of the Republic of Cyprus. c. Excludes Abkhazia and South Ossetia. d. Excludes Transnistria. e. Includes Former Spanish Sahara. f. Excludes South Sudan after July 9, 2011. g. Covers mainland Tanzania only.

72 

World Development Indicators 2013

Front

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People

Environment


Economy 4 About the data Economic data are organized by several different accounting con-

this year’s edition are not comparable with those from earlier edi-

ventions: the system of national accounts, the balance of pay-

tions with different base years.

ments, government finance statistics, and international finance

Rescaling may result in a discrepancy between the rescaled GDP

statistics. There has been progress in unifying the concepts in the

and the sum of the rescaled components. To avoid distortions in the

system of national accounts, balance of payments, and government

growth rates, the discrepancy is left unallocated. As a result, the

finance statistics, but there are many national variations in the

weighted average of the growth rates of the components generally

implementation of these standards. For example, even though the

does not equal the GDP growth rate.

United Nations recommends using the 2008 System of National Accounts (2008 SNA) methodology in compiling national accounts,

Adjusted net savings

many are still using earlier versions, some as old as 1968. The

Adjusted net savings measure the change in a country’s real wealth

International Monetary Fund (IMF) has recently published a new

after accounting for the depreciation and depletion of a full range

balance of payments methodology (BPM6), but many countries are

of assets in the economy. If a country’s adjusted net savings are

still using the previous version. Similarly, the standards and defini-

positive and the accounting includes a sufficiently broad range of

tions for government finance statistics were updated in 2001, but

assets, economic theory suggests that the present value of social

several countries still report using the 1986 version. For individual

welfare is increasing. Conversely, persistently negative adjusted

country information about methodology used, refer to Primary data

net savings indicate that the present value of social welfare is

documentation.

decreasing, suggesting that an economy is on an unsustainable path.

Economic growth

Adjusted net savings are derived from standard national account-

An economy’s growth is measured by the change in the volume of its

ing measures of gross savings by making four adjustments. First,

output or in the real incomes of its residents. The 2008 SNA offers

estimates of fixed capital consumption of produced assets are

three plausible indicators for calculating growth: the volume of gross

deducted to obtain net savings. Second, current public expendi-

domestic product (GDP), real gross domestic income, and real gross

tures on education are added to net savings (in standard national

national income. Only growth in GDP is reported here.

accounting these expenditures are treated as consumption). Third,

Growth rates of GDP and its components are calculated using the

estimates of the depletion of a variety of natural resources are

least squares method and constant price data in the local currency

deducted to reflect the decline in asset values associated with their

for countries and using constant price U.S. dollar series for regional

extraction and harvest. And fourth, deductions are made for dam-

and income groups. Local currency series are converted to constant

ages from carbon dioxide emissions and local pollution. By account-

U.S. dollars using an exchange rate in the common reference year.

ing for the depletion of natural resources and the degradation of

The growth rates are average annual and compound growth rates.

the environment, adjusted net savings goes beyond the definition

Methods of computing growth are described in Statistical methods.

of savings or net savings in the SNA.

Forecasts of growth rates come from World Bank (2013).

Balance of payments Rebasing national accounts

The balance of payments records an economy’s transactions with

Rebasing of national accounts can alter the measured growth rate of

the rest of the world. Balance of payments accounts are divided

an economy and lead to breaks in series that affect the consistency

into two groups: the current account, which records transactions

of data over time. When countries rebase their national accounts,

in goods, services, primary income, and secondary income, and

they update the weights assigned to various components to better

the capital and financial account, which records capital transfers,

reflect current patterns of production or uses of output. The new

acquisition or disposal of nonproduced, nonfinancial assets, and

base year should represent normal operation of the ­economy—it

transactions in financial assets and liabilities. The current account

should be a year without major shocks or distortions. Some devel-

balance is one of the most analytically useful indicators of an exter-

oping countries have not rebased their national accounts for many

nal imbalance.

years. Using an old base year can be misleading because implicit

A primary purpose of the balance of payments accounts is to

price and volume weights become progressively less relevant and

indicate the need to adjust an external imbalance. Where to draw

useful.

the line for analytical purposes requires a judgment concerning the

To obtain comparable series of constant price data for comput-

imbalance that best indicates the need for adjustment. There are a

ing aggregates, the World Bank rescales GDP and value added by

number of definitions in common use for this and related analytical

industrial origin to a common reference year. This year’s World Devel-

purposes. The trade balance is the difference between exports and

opment Indicators continues to use 2000 as the reference year.

imports of goods. From an analytical view it is arbitrary to distinguish

Because rescaling changes the implicit weights used in forming

goods from services. For example, a unit of foreign exchange earned

regional and income group aggregates, aggregate growth rates in

by a freight company strengthens the balance of payments to the

Economy

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World Development Indicators 2013 73


4 Economy same extent as the foreign exchange earned by a goods exporter.

Government finance statistics are reported in local currency. The

Even so, the trade balance is useful because it is often the most

indicators here are shown as percentages of GDP. Many countries

timely indicator of trends in the current account balance. Customs

report government finance data by fiscal year; see Primary data

authorities are typically able to provide data on trade in goods long

documentation for information on fiscal year end by country.

before data on trade in services are available. Beginning in August 2012, the International Monetary Fund imple-

Financial accounts

mented the Balance of Payments Manual 6 (BPM6) framework in

Money and the financial accounts that record the supply of money

its major statistical publications. The World Bank will implement

lie at the heart of a country’s financial system. There are several

BPM6 in its online databases and publications in April 2013. Bal-

commonly used definitions of the money supply. The narrowest, M1,

ance of payments data for 2005 onward will be presented in accord

encompasses currency held by the public and demand deposits with

with the BPM6. The historical BPM5 data series will end with data

banks. M2 includes M1 plus time and savings deposits with banks

for 2008, which can be accessed through the World Development

that require prior notice for withdrawal. M3 includes M2 as well as

Indicators archives.

various money market instruments, such as certificates of deposit

The complete balance of payments methodology can be accessed

issued by banks, bank deposits denominated in foreign currency,

through the International Monetary Fund website (www.imf.org/

and deposits with financial institutions other than banks. However

external/np/sta/bop/bop.htm).

defined, money is a liability of the banking system, distinguished from other bank liabilities by the special role it plays as a medium

Government finance

of exchange, a unit of account, and a store of value.

Central government cash surplus or deficit, a summary measure of

A general and continuing increase in an economy’s price level is

the ongoing sustainability of government operations, is comparable

called inflation. The increase in the average prices of goods and

to the national accounting concept of savings plus net capital trans-

services in the economy should be distinguished from a change

fers receivable, or net operating balance in the 2001 update of the

in the relative prices of individual goods and services. Generally

IMF’s Government Finance Statistics Manual.

accompanying an overall increase in the price level is a change in

The 2001 manual, harmonized with the 1993 SNA, recommends

the structure of relative prices, but it is only the average increase,

an accrual accounting method, focusing on all economic events

not the relative price changes, that constitutes inflation. A commonly

affecting assets, liabilities, revenues, and expenses, not just those

used measure of inflation is the consumer price index, which mea-

represented by cash transactions. It accounts for all changes in

sures the prices of a representative basket of goods and services

stocks, so stock data at the end of an accounting period equal stock

purchased by a typical household. The consumer price index is usu-

data at the beginning of the period plus flows over the period. The

ally calculated on the basis of periodic surveys of consumer prices.

1986 manual considered only debt stocks.

Other price indices are derived implicitly from indexes of current and

For most countries central government finance data have been

74 

constant price series.

consolidated into one account, but for others only budgetary central

Consumer price indexes are produced more frequently and so

government accounts are available. Countries reporting budgetary

are more current. They are constructed explicitly, using surveys

data are noted in Primary data documentation. Because budgetary

of the cost of a defined basket of consumer goods and services.

accounts may not include all central government units (such as

Nevertheless, consumer price indexes should be interpreted with

social security funds), they usually provide an incomplete picture.

caution. The definition of a household, the basket of goods, and the

In federal states the central government accounts provide an incom-

geographic (urban or rural) and income group coverage of consumer

plete view of total public finance.

price surveys can vary widely by country. In addition, weights are

Data on government revenue and expense are collected by the IMF

derived from household expenditure surveys, which, for budgetary

through questionnaires to member countries and by the Organisa-

reasons, tend to be conducted infrequently in developing countries,

tion for Economic Co-operation and Development (OECD). Despite

impairing comparability over time. Although useful for measuring

IMF efforts to standardize data collection, statistics are often incom-

consumer price inflation within a country, consumer price indexes

plete, untimely, and not comparable across countries.

are of less value in comparing countries.

World Development Indicators 2013

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Economy 4 Definitions

on education expenditure from the United Nations Educational,

• Gross domestic product (GDP) at purchaser prices is the sum of

Scientific, and Cultural Organization Institute for Statistics online

gross value added by all resident producers in the economy plus any

database, with missing data estimated by World Bank staff; data on

product taxes (less subsidies) not included in the valuation of output.

forest, energy, and mineral depletion based on sources and meth-

It is calculated without deducting for depreciation of fabricated capital

ods in World Bank (2011); data on carbon dioxide damage from

assets or for depletion and degradation of natural resources. Value

Fankhauser (1994); data on local pollution damage from Pandey and

added is the net output of an industry after adding up all outputs and

others (2006). Data on current account balance are from the IMF’s

subtracting intermediate inputs. • Gross savings are the difference

Balance of Payments Statistics Yearbook and International Financial

between gross national income and public and private consump-

Statistics. Data on central government finances are from the IMF’s

tion, plus net current transfers. • Adjusted net savings measure

Government Finance Statistics database. Data on the consumer

the change in value of a specified set of assets, excluding capital

price index are from the IMF’s International Financial Statistics. Data

gains. Adjusted net savings are net savings plus education expendi-

on broad money are from the IMF’s monthly International Financial

ture minus energy depletion, mineral depletion, net forest depletion,

Statistics and annual International Financial Statistics Yearbook.

and carbon dioxide and particulate emissions damage. • Current account balance is the sum of net exports of goods and services, net

References

primary income, and net secondary income. • Central government

Asian Development Bank. 2012. Asian Development Outlook 2012

cash surplus or deficit is revenue (including grants) minus expense,

Update: Services and Asia’s Future Growth. Manila.

minus net acquisition of nonfinancial assets. In editions before 2005

De la Torre, Augusto, Tatiana Didier, and Samuel Pienknagura. 2012.

nonfinancial assets were included under revenue and expenditure in

Latin America Copes with Volatility, the Dark Side of Globalization.

gross terms. This cash surplus or deficit is close to the earlier overall budget balance (still missing is lending minus repayments, which are

Washington, DC: World Bank. Fankhauser, Samuel. 1994. “The Social Costs of Greenhouse Gas Emis-

included as a financing item under net acquisition of financial assets).

sions: An Expected Value Approach.” Energy Journal 15 (2): 157–84.

• Central government debt is the entire stock of direct government

Hamilton, Kirk, and Michael Clemens. 1999. “Genuine Savings Rates in

fixed-term contractual obligations to others outstanding on a particu-

Developing Countries.” World Bank Economic Review 13 (2): 333–56.

lar date. It includes domestic and foreign liabilities such as currency

IMF (International Monetary Fund). 2001. Government Finance Statis-

and money deposits, securities other than shares, and loans. It is

tics Manual. Washington, DC.

the gross amount of government liabilities reduced by the amount

———. 2012. Regional Economic Outlook: Sub-­Saharan Africa—Main-

of equity and financial derivatives held by the government. Because

taining Growth in an Uncertain World, October 2012. Washington, DC.

debt is a stock rather than a flow, it is measured as of a given date,

www.imf.org/external/pubs/ft/reo/2012/afr/eng/sreo1012.htm.

usually the last day of the fiscal year. • Consumer price index reflects

Pandey, Kiran D., Katharine Bolt, Uwe Deichmann, Kirk Hamilton, Bart

changes in the cost to the average consumer of acquiring a basket

Ostro, and David Wheeler. 2006. “The Human Cost of Air Pollution:

of goods and services that may be fixed or may change at specified

New Estimates for Developing Countries.” World Bank, Development

intervals, such as yearly. The Laspeyres formula is generally used.

Research Group and Environment Department, Washington, DC.

• Broad money (IFS line 35L..ZK) is the sum of currency outside

United Nations Statistics Division. Various years. National Accounts

banks; demand deposits other than those of the central government;

Statistics: Main Aggregates and Detailed Tables. Parts 1 and 2. New

the time, savings, and foreign currency deposits of resident sectors other than the central government; bank and traveler’s checks; and other securities such as certificates of deposit and commercial paper.

York: United Nations. World Bank. 2011. The Changing Wealth of Nations: Measuring Sustainable Development for the New Millennium. Washington, DC. ———. 2012a. East Asia and Pacific Economic Update 2012, Volume 2:

Data sources

Remaining Resilient. Washington, DC.

Data on GDP for most countries are collected from national statisti-

———. 2012b. Middle East and North Africa Region Economic Devel-

cal organizations and central banks by visiting and resident World

opments and Prospects, October 2012: Looking Ahead After a Year

Bank missions; data for selected high-income economies are from

in Transition. Washington, DC.

the OECD. Data on gross savings are from World Bank national

———. 2012c. South Asia Economic Focus—A Review of Economic

accounts data files. Data on adjusted net savings are based on

Developments in South Asian Countries: Creating Fiscal Space

a conceptual underpinning by Hamilton and Clemens (1999) and calculated using data on consumption of fixed capital from the United Nations Statistics Division’s National Accounts Statistics: Main Aggregates and Detailed Tables, extrapolated to 2010; data

Economy

States and markets

through Revenue Mobilization. Washington, DC. ———. 2013. Global Economic Prospects, Volume 6, January 13: Assuring Growth over the Medium Term. Washington, DC. ———. Various years. World Development Indicators. Washington, DC.

Global links

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World Development Indicators 2013 75


4 Economy Online tables and indicators To access the World Development Indicators online tables, use

indicator online, use the URL http://data.worldbank.org/indicator/

the URL http://wdi.worldbank.org/table/ and the table number (for

and the indicator code (for example, http://data.worldbank.org/

example, http://wdi.worldbank.org/table/4.1). To view a specific

indicator/NY.GDP.MKTP.KD.ZG).

4.1 Growth of output

4.7 Structure of service imports

Gross domestic product

NY.GDP.MKTP.KD.ZG

Commercial service imports

TM.VAL.SERV.CD.WT

Agriculture

NV.AGR.TOTL.KD.ZG

Transport

TM.VAL.TRAN.ZS.WT

Travel

TM.VAL.TRVL.ZS.WT TM.VAL.INSF.ZS.WT

Industry

NV.IND.TOTL.KD.ZG

Manufacturing

NV.IND.MANF.KD.ZG

Insurance and financial services

Services

NV.SRV.TETC.KD.ZG

Computer, information, communications, and other commercial services

TM.VAL.OTHR.ZS.WT

4.2 Structure of output Gross domestic product

NY.GDP.MKTP.CD

Agriculture

NV.AGR.TOTL.ZS

Industry

NV.IND.TOTL.ZS

Manufacturing

NV.IND.MANF.ZS

Services

NV.SRV.TETC.ZS

4.3 Structure of manufacturing Manufacturing value added

NV.IND.MANF.CD

Food, beverages and tobacco

NV.MNF.FBTO.ZS.UN

Textiles and clothing

NV.MNF.TXTL.ZS.UN

Machinery and transport equipment

NV.MNF.MTRN.ZS.UN

Chemicals

NV.MNF.CHEM.ZS.UN

Other manufacturing

NV.MNF.OTHR.ZS.UN

4.4 Structure of merchandise exports Merchandise exports

TX.VAL.MRCH.CD.WT

Food

TX.VAL.FOOD.ZS.UN

Agricultural raw materials

TX.VAL.AGRI.ZS.UN

Fuels

TX.VAL.FUEL.ZS.UN

Household final consumption expenditure

NE.CON.PETC.ZS

General government final consumption expenditure

NE.CON.GOVT.ZS

Gross capital formation

NE.GDI.TOTL.ZS

Exports of goods and services

NE.EXP.GNFS.ZS

Imports of goods and services

NE.IMP.GNFS.ZS

Gross savings

NY.GNS.ICTR.ZS

4.9 Growth of consumption and investment Household final consumption expenditure

NE.CON.PRVT.KD.ZG

Household final consumption expenditure, Per capita NE.CON.PRVT.PC.KD.ZG General government final consumption expenditure Gross capital formation

NE.CON.GOVT.KD.ZG NE.GDI.TOTL.KD.ZG

Exports of goods and services

NE.EXP.GNFS.KD.ZG

Imports of goods and services

NE.IMP.GNFS.KD.ZG

4.10 Toward a broader measure of national income

Ores and metals

TX.VAL.MMTL.ZS.UN

Gross domestic product, $

NY.GDP.MKTP.CD

Manufactures

TX.VAL.MANF.ZS.UN

Gross national income, $

NY.GNP.MKTP.CD

4.5 Structure of merchandise imports Merchandise imports

TM.VAL.MRCH.CD.WT

Food

TM.VAL.FOOD.ZS.UN

Agricultural raw materials

TM.VAL.AGRI.ZS.UN

Fuels

TM.VAL.FUEL.ZS.UN

Ores and metals

TM.VAL.MMTL.ZS.UN

Manufactures

TM.VAL.MANF.ZS.UN

Commercial service exports

TX.VAL.SERV.CD.WT

Transport

TX.VAL.TRAN.ZS.WT

Travel

TX.VAL.TRVL.ZS.WT

Insurance and financial services

TX.VAL.INSF.ZS.WT

Computer, information, communications, and other commercial services

World Development Indicators 2013

Front

Consumption of fixed capital

NY.ADJ.DKAP.GN.ZS

Natural resource depletion

NY.ADJ.DRES.GN.ZS

Adjusted net national income

NY.ADJ.NNTY.CD

Gross domestic product, % growth

NY.GDP.MKTP.KD.ZG

Gross national income, % growth

NY.GNP.MKTP.KD.ZG

Adjusted net national income

NY.ADJ.NNTY.KD.ZG

4.11 Toward a broader measure of savings Gross savings

4.6 Structure of service exports

76 

4.8 Structure of demand

TX.VAL.OTHR.ZS.WT

?

User guide

NY.ADJ.ICTR.GN.ZS

Consumption of fixed capital

NY.ADJ.DKAP.GN.ZS

Education expenditure

NY.ADJ.AEDU.GN.ZS

Net forest depletion

NY.ADJ.DFOR.GN.ZS

Energy depletion

NY.ADJ.DNGY.GN.ZS

Mineral depletion

NY.ADJ.DMIN.GN.ZS

Carbon dioxide damage

NY.ADJ.DCO2.GN.ZS

Local pollution damage

NY.ADJ.DPEM.GN.ZS

Adjusted net savings

NY.ADJ.SVNG.GN.ZS

World view

People

Environment


Economy 4 4.12 Central government finances

4.15 Monetary indicators

Revenue

GC.REV.XGRT.GD.ZS

Broad money

Expense

GC.XPN.TOTL.GD.ZS

Claims on domestic economy

FM.LBL.BMNY.ZG

Cash surplus or deficit

GC.BAL.CASH.GD.ZS

Claims on central governments

Net incurrence of liabilities, Domestic

FM.AST.DOMO.ZG.M3 FM.AST.CGOV.ZG.M3

GC.FIN.DOMS.GD.ZS

Interest rate, Deposit

FR.INR.DPST

Net incurrence of liabilities, Foreign

GC.FIN.FRGN.GD.ZS

Interest rate, Lending

FR.INR.LEND

Debt and interest payments, Total debt

GC.DOD.TOTL.GD.ZS

Interest rate, Real

FR.INR.RINR

Debt and interest payments, Interest

GC.XPN.INTP.RV.ZS

4.13 Central government expenditure

Official exchange rate

Goods and services

GC.XPN.GSRV.ZS

Compensation of employees

GC.XPN.COMP.ZS

Interest payments

GC.XPN.INTP.ZS

Subsidies and other transfers

GC.XPN.TRFT.ZS

Other expense

GC.XPN.OTHR.ZS

4.14 Central government revenues Taxes on income, profits and capital gains

GC.TAX.YPKG.RV.ZS

Taxes on goods and services

GC.TAX.GSRV.RV.ZS

Taxes on international trade

GC.TAX.INTT.RV.ZS

Other taxes

4.16 Exchange rates and price

GC.TAX.OTHR.RV.ZS

Social contributions

GC.REV.SOCL.ZS

Grants and other revenue

GC.REV.GOTR.ZS

PA.NUS.FCRF

Purchasing power parity (PPP) conversion factor

States and markets

PA.NUS.PPPC.RF

Real effective exchange rate

PX.REX.REER

GDP implicit deflator

NY.GDP.DEFL.KD.ZG

Consumer price index

FP.CPI.TOTL.ZG

Wholesale price index

FP.WPI.TOTL

4.17 Balance of payments current account Goods and services, Exports

BX.GSR.GNFS.CD

Goods and services, Imports

BM.GSR.GNFS.CD

Balance on primary income

BN.GSR.FCTY.CD

Balance on secondary income

BN.TRF.CURR.CD

Current account balance

BN.CAB.XOKA.CD

Total reserves

Economy

PA.NUS.PPP

Ratio of PPP conversion factor to market exchange rate

Global links

FI.RES.TOTL.CD

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World Development Indicators 2013 77


STATES AND MARKETS 78â&#x20AC;&#x192;

World Development Indicators 2013

Front

?

User guide

World view

People

Environment


States and markets includes indicators of private sector investment and performance, the role of the public sector in nurturing investment and growth, and the quality and availability of infrastructure essential for growth and development. These indicators measure the business environment, the functions of government, financial system development, infrastructure, information and communication technology, science and technology, performance of governments and their policies, and conditions in fragile countries with weak institutions. Measures of investment in infrastructure projects with private participation show the private sector’s contributions to providing public services and easing fiscal constraints. For example, private investment in the dynamic telecommunications sector has increased more than 50 percent since 2006, reaching $158.5 billion in 2011. Data on access to finance, availability of credit, cost of service, and stock markets help improve understanding of the state of financial development. In 2011 people had greater access to finance in Europe and Central Asia, with 19 commercial bank branches and 47 automated teller machines per 100,000 people, than in other developing regions. Stock market measures show the effects of the global financial crises: the market capitalization of listed companies dropped from $64.6 trillion in 2007 to $34.9  trillion in 2008, recovering to only $46.8 trillion in 2011. Economic health is measured not only in macroeconomic terms but also by laws, regulations, and institutional arrangements. Firms

Economy

States and markets

evaluating investment options, governments interested in improving business conditions, and economists seeking to explain economic performance have all grappled with defining and measuring the business environment and how constraints affect productivity and job creation. The World Development Indicators database includes results from enterprise surveys and expert assessments of the business environment from the Doing Business project. States and markets also includes data on the size of the transportation and power sectors, as well as the spread of new information technology. To become competitive suppliers to the rest of the world, many developing countries need to improve their road, rail, port, and air transport facilities. Expanding electricity supply to meet the growing demand of increasingly urban and industrialized economies without unacceptable social, economic, and environmental costs is one of the greatest challenges facing developed and developing countries. Data on electric power consumption per capita show that consumption in developing countries has doubled since 1995, to 1,660 kilowatt-hours in 2010. With rapid growth of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential for development. World Development Indicators includes data showing the rapid changes in this sector. For instance, mobile cellular subscriptions increased from 16 percent of the global population in 2001 to 85 percent in 2011, and Internet users from 8 percent to 33 percent over the same period.

Global links

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5

World Development Indicators 2013 79


Highlights East Asia & Pacific: Patent applications are rising, especially in China Patent applications, 2011 (thousands)

W hile the global economy continued to underperform, intellectual

600

property filings worldwide grew strongly in 2011. Demand for patents rose from 800,000 applications in the early 1980s to 2.14 million in 2011, topping 2 million for the first time. After dropping 4 percent in 2009, patent applications rebounded strongly in 2010 and 2011, aver-

400

aging 7 percent growth a year. In 2011 China’s patent office became the world’s largest, measured by patent applications received, according to World Intellectual Property Organization (2012). China received

200

526,412 applications, compared with 503,582 for the United States and 342,610 for Japan. Of the 20 busiest patent offices, 7 were in East Asia and Pacific, accounting for 58 percent of total applications

Un C ite hin dS a tat es Ko Jap rea an ,R Ge ep. rm Ru an ss y ian Fe Ind de ia rat Ca ion Un A nad ite us a d K tra ing lia do Ho m Fra ng n Ko ng M ce SA exi R, co C Sin hin ga a po re So It uth aly Af ric a Is M rae Ne ala l w ysi Ze a a Ind land on es ia

0

filed worldwide in 2011.

Source: Online table 5.13.

Europe & Central Asia: Container traffic picks up Container port traffic (millions of twenty-foot equivalent units)

M easures of port container traffic, much of it commodity shipments of medium to high value added, give some indication of a country’s

8

economic status, though much of the economic benefit from transshipments goes to the terminal operator and ancillary services for ships and containers rather than to the country more broadly. After

6

the 2008 fiscal crisis, worldwide container shipments fell to 472 million in 2009, an almost 9 percent drop from 2008, affecting

4

all ports, operators, and countries. But shipments rebounded in 2010, growing 15 percent and reaching precrisis levels. Most of the growth came from intercontinental shipments by developing coun-

Lithuania

Romania

tries. In 2011 the Russian Federation (3.8 million) and Turkey

2011

0

2009

2

Russian Federation

(6.1 million) led the market in container shipments in Europe and Turkey

Ukraine

Central Asia.

Source: Online table 5.10.

Latin America & Caribbean: Homicide rates mount Intentional homicides, 2011 or latest available (per 100,000 people)

Homicide rates are very high in Latin America and the Caribbean and Sub-­Saharan Africa, where they add to the death toll caused by armed

100

conflicts. According to the Geneva Declaration on Armed Violence and Development (2011), a quarter of all violent deaths occur in just 14

75

countries, averaging more than 30 violent deaths per 100,000 people a year, half of them in Latin America and the Caribbean. In many of

50

these countries, homicides, not armed conflicts, account for the majority of violent deaths. The links between violent death rates and socioeconomic development show that homicide rates are higher

25

where income disparity, extreme poverty, and hunger are high. Countries that have strengthened their rule of law have seen a decline in

Ho nd El uras Sa l Cô vado te r d Ve ’Ivo ne i r e zu ela ,R B Be liz Ja e m Gu aica St ate .K m itts ala & Ne vis Za mb ia Ug an da Ma law i Tri nid Les ad oth o & To So bag uth o Af ric a

0

homicide rates.

Source: Online table 5.8.

80 

World Development Indicators 2013

Front

?

User guide

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People

Environment


Middle East & North Africa: Military spending share continues to rise Demanding open government and good governance from their head

Military spending (% of GDP)

of states, citizens of countries in the Middle East and North Africa

4

brought about the Arab Spring in late 2010 and 2011. Although it is still too soon to estimate the effects of the turbulent years, there 3

are signs of higher military spending and arms imports. From 2010 to 2011 all regions except the Middle East and North Africa reduced military spending as a share of gross domestic product (GDP). ­Algeria

2

increased military spending from 3.5 percent of GDP in 2010 to 4.6 percent in 2011, and Tunisia from 1.2 percent of GDP in 2010

2010

(6.8 percent), both high-income economies, spend the most on the 0

military, followed by Iraq (5.1 percent), Jordan (4.7 percent), and Leba-

2011

1

to 1.3 percent in 2011. Saudi Arabia (8.4 percent of GDP) and Israel

Europe & Central Asia

East Asia & Pacific

non (4.4 percent).

Middle East & North Africa

Latin America & Caribbean

South Asia

Sub-Saharan Africa

Source: Online table 5.7.

South Asia: Mobile phone access growing rapidly Mobile phone subscriptions have roughly doubled every two years since 2002 and now exceed the number of fixed-line subscriptions in 2002. By the end of 2011 there were 5.9 billion mobile phone

Mobile phone subscriptions (per 100 people) 150 Europe & Central Asia

subscriptions worldwide, almost one for every person if distributed equally. Developing economies have lagged behind, but they are catching up. Sub-­S aharan Africa, where 53 per 100 people have mobile

Latin America & Caribbean 100

Middle East & North Africa

phone subscriptions, started far behind but has reached the same

High income

subscription rate as high-income economies did 11 years ago. South Asia is only eight years behind. In recent years South Asia has had the largest growth in mobile subscription coverage among developing

East Asia & Pacific 50 Sub-Saharan Africa

regions, with 69 mobile phone subscriptions per 100 people in 2011, up from 8 in 2005. 0

South Asia 2000

2002

2004

2006

2008

2010 2011

Source: Online table 5.11.

Sub-­Saharan Africa: Growth with good policies The World Bank’s Country Policy and Institutional Assessment (CPIA) score reflects country performance in promoting economic growth and reducing poverty. Data for Sub-­Saharan countries show a positive

Average GDP growth, 2006–11 (%) 15

association between average CPIA score and average GDP growth over 2006–11. In 2011 the region’s average CPIA score for International Development Association countries was 3.2 on a scale of 1 (low) to

10

6 (high). The regional average masks the wide variation across countries, from 2.2 in Eritrea and Zimbabwe to 4.0 in Cape Verde. For several countries the policy environment is the best in recent years.

5

Thirteen countries saw an improvement in the 2011 score by at least 0.1, 20 countries saw no change, and 5 saw a decline of 0.1 or more (World Bank 2012).

0 2.0

2.5

3.0

3.5

4.0

Average CPIA score, 2006–11 (1, low, to 6, high) Source: Online table 5.9.

Economy

States and markets

Global links

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World Development Indicators 2013 81


5  States and markets Business Time Stock market Domestic Tax revenue Military Electric Mobile Individuals High-technology entry required capitalization credit collected expenditures power cellular using the exports density to start a provided by central consumption subscriptionsa Interneta by banking government per capita per 1,000 business sector people days

% of GDP

% of GDP

% of GDP

% of GDP

kilowatt-hours

per 100 people

2011

June 2012

2010

2011

% of % of manufactured population exports

2011

2011

2011

2011

Afghanistan

0.12

7

..

–2.9

8.7

4.7

..

54

5

..

Albania

0.96

4

..

69.1

..

1.5

1,770

96

49

0.5

Algeria

0.19

25

..

–4.7

4.6

1,026

99

14

0.2

American Samoa

..

..

..

..

..

..

..

..

..

..

Andorra

..

..

..

..

..

..

..

75

81

..

Angola

..

68

..

17.9

..

3.5

248

48

15

..

Antigua and Barbuda

..

21

..

101.4

..

..

..

196

82

0.0

Argentina

0.46

26

9.8

31.3

..

0.7

2,904

135

48

8.0

Armenia

1.12

8

0.4

36.0

17.0

4.0

1,606

104

32

2.6

..

..

..

..

..

..

..

123

57

4.6

Australia

6.17

2

86.9

145.1

20.6b

1.9

10,286

108

79

13.0

Austria

0.56

25

19.7

135.3

18.4b

0.9

8,356

155

80

11.9

Azerbaijan

0.63

8

..

20.0

12.7

4.9

1,603

109

50

1.3

Bahamas, The

..

31

..

108.1

16.8

..

..

86

65

0.0

Bahrain

..

9

89.0

75.2

..

3.4

9,814

128

77

0.2

Aruba

Bangladesh Barbados

39.4b

2011

2011

0.10

19

21.0

70.4

10.0

1.3

279

56

5

..

..

18

124.1

136.3

27.2

..

..

127

72

13.7

Belarus

0.91

5

..

34.4

16.3

1.1

3,564

112

40

2.6

Belgium

3.00

4

44.8

116.9

24.6b

1.1

8,388

117

78

10.0

Belize

4.54

44

..

66.9

22.3

1.1

..

70

14

0.4

Benin

..

26

..

21.7

15.7b

1.0

90

85

4

0.5

Bermuda

..

..

26.6

..

..

..

..

136

88

..

Bhutan

0.06

36

..

49.2

9.2

..

..

66

21

0.1

Bolivia

0.48

50

17.2

48.6

..

1.5

616

83

30

13.7

Bosnia and Herzegovina

0.71

37

..

57.7

20.9b

1.4

3,110

85

60

3.0

Botswana

9.44

61

23.7

7.1

20.8

2.1

1,586

143

7

0.9

Brazil

2.38

119

49.6

98.3

15.7

1.4

2,384

124

45

9.7

..

101

..

8.1

..

2.5

8,759

109

56

..

Bulgaria

7.20

18

15.4

71.4

19.1

1.5

4,476

141

51

7.5

Burkina Faso

0.11

13

..

17.6

14.2b

1.3

..

45

3

5.9

..

8

..

28.3

2.7

..

22

1

8.5

Cambodia

0.22

85

..

24.2

1.5

146

96

3

0.1

Cameroon

..

15

..

14.0

1.4

271

52

5

4.9

Brunei Darussalam

Burundi

.. 10.0 b ..

7.56

5

109.8

177.6

11.9b

1.4

15,137

80

83

13.4

Cape Verde

..

11

..

80.6

19.8

0.5

..

79

32

0.6

Cayman Islands

..

..

..

..

..

..

..

168

69

..

Central African Republic

..

22

..

25.4

..

2.6

..

41

2

0.0

Chad

..

62

..

7.0

..

2.3

..

32

2

..

Channel Islands

..

..

..

..

..

..

..

..

..

..

4.13

8

108.7

71.2

19.1b

3.2

3,297

130

54

4.6

..

33

46.3

145.5

10.5

2.0 c

Canada

Chile China Hong Kong SAR, China

2,944

73

38

25.8

b

..

5,923

215

75

13.7

27.67

3

357.8

207.1

13.5

..

..

..

–28.1

37.9b

..

..

243

58

0.0

Colombia

1.80

13

60.4

65.6

13.9b

3.3

1,012

98

40

4.3

Comoros

..

20

..

21.2

..

..

..

29

6

..

Congo, Dem. Rep.

..

58

..

3.3

13.7

1.5

95

23

1

..

Congo, Rep.

..

161

..

–16.1

..

1.1

145

94

6

3.7

Macao SAR, China

82 

ages 15–64

World Development Indicators 2013

Front

?

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People

Environment


States and markets  5 Business Time Stock market Domestic Tax revenue Military Electric Mobile Individuals High-technology entry required capitalization credit collected expenditures power cellular using the exports density to start a provided by central consumption subscriptionsa Interneta by banking government per capita per 1,000 business sector people ages 15–64

days

% of GDP

2011

June 2012

% of GDP

% of GDP

% of GDP 2011

kilowatt-hours

per 100 people

2010

2011

2011

2011

2011

17.64

60

3.5

53.3

13.8b

..

..

32

26.1

25.3

..b

1.5

210

2.39

9

34.9

90.3

18.4

1.7

3,813

Cuba

..

..

..

..

..

3.3

1,299

Curaçao

..

..

..

..

..

..

24.73

8

11.6

330.1

Czech Republic

2.84

20

17.7

67.4

Denmark

4.55

6

53.8

205.4

Costa Rica Côte d’Ivoire Croatia

Cyprus

Djibouti

% of % of manufactured population exports 2011

2011

42

40.8

86

2

15.1

116

71

7.6

12

23

..

..

..

..

..

2.2

4,675

98

58

27.3

13.7

1.1

6,321

123

73

16.0

33.8b

1.5

6,327

128

90

13.9

26.1b

1,855

92

..

37

..

32.3

..

3.7

..

21

7

0.1

Dominica

3.30

13

..

57.9

..

..

..

164

51

0.0

Dominican Republic

0.96

19

..

43.8

..

56

8.8

28.3

Ecuador

12.7b

0.6

1,442

87

36

2.2

..

3.5

1,055

105

31

3.2

Egypt, Arab Rep.

0.13

7

21.2

74.9

14.1

1.9

1,608

101

39

0.7

El Salvador

0.46

17

23.7

66.5

13.5b

1.0

855

134

18

5.8

Equatorial Guinea

..

135

..

–3.0

..

..

..

59

6

..

Eritrea

..

84

..

104.0

..

..

52

4

6

..

Estonia

8.10

7

7.3

85.8

16.0 b

1.7

6,464

139

77

13.4

Ethiopia

2.0

0.03

15

..

37.1

9.3

1.1

54

17

1

Faeroe Islands

..

..

..

..

..

..

..

122

81

0.6

Fiji

..

58

35.9

114.3

..

1.6

..

84

28

4.5

Finland

3.60

14

54.4

101.6

20.6b

1.5

16,483

166

89

9.3

France

3.13

7

56.6

133.5

21.3b

2.2

7,729

95

80

23.7 15.8

French Polynesia Gabon Gambia, The

..

..

..

..

..

..

..

81

49

4.27

58

..

12.2

..

0.9

1,004

117

8

3.0

..

27

..

43.8

..

..

..

79

11

3.3

Georgia

4.49

2

5.5

34.3

23.9

3.0

1,743

102

37

1.8

Germany

1.35

15

32.9

124.8

11.8b

1.3

7,215

132

83

15.0

Ghana

1.09

12

7.9

27.7

Greece

0.85

11

11.6

153.2

Greenland

..

..

..

..

Grenada

..

15

..

91.3

Guam

15.0

0.3

298

85

14

1.4

21.3b

2.8

5,242

106

53

9.7

..

..

..

103

64

..

18.3

..

..

117

33

11.1

..

..

..

..

..

..

..

..

51

..

0.64

40

..

39.2

11.0

0.4

567

140

12

4.4

Guinea

..

35

..

32.3

..

..

..

44

1

0.1

Guinea-Bissau

..

9

..

13.6

..

..

..

56

3

..

Guyana

..

20

17.1

51.0

..

1.2

..

70

32

0.1

Haiti

..

105

..

17.4

..

24

41

8

..

Honduras

..

14

..

53.3

15.0 b

1.1

671

104

16

1.3

Hungary

7.63

5

13.4

75.7

21.0 b

1.0

3,876

117

59

22.7

Iceland

7.94

5

14.4

148.2

22.3

0.1

51,440

106

95

20.9

India

0.09

27

54.2

74.1

10.4

2.5

616

72

10

6.9

Indonesia

0.27

47

46.1

38.5

11.8

0.7

641

103

18

8.3 4.5

Guatemala

Iran, Islamic Rep.

..

..

13

19.1

37.4

9.3

1.9

2,652

75

21

Iraq

0.11

74

..

–0.8

..

5.1

1,183

78

5

..

Ireland

4.78

10

16.3

225.7

0.6

6,025

108

77

21.2

Isle of Man Israel

Economy

11.65

..

..

..

4.46

21

59.7

85.9

States and markets

23.1b .. 24.5b

Global links

..

..

..

..

..

6.8

6,856

122

70

14.0

Back

World Development Indicators 2013 83


5  States and markets Business Time Stock market Domestic Tax revenue Military Electric Mobile Individuals High-technology entry required capitalization credit collected expenditures power cellular using the exports density to start a provided by central consumption subscriptionsa Interneta by banking government per capita per 1,000 business sector people days

% of GDP

2011

June 2012

% of GDP

% of GDP

% of GDP

kilowatt-hours

per 100 people

% of % of manufactured population exports

2011

2011

2011

2011

2010

2011

2011

Italy

1.63

6

19.7

157.0

22.5b

1.6

5,384

158

57

7.4

Jamaica

1.10

7

50.0

49.6

25.6b

0.8

1,223

108

32

0.6

Japan

1.10

23

60.3

341.7

9.8b

1.0

8,394

105

80

17.5

Jordan

0.83

12

94.3

106.7

15.0

4.7

2,226

118

35

2.5

Kazakhstan

1.64

19

23.0

40.3

22.5

1.0

4,728

156

45

29.9

Kenya

0.85

32

30.3

52.0

19.9

1.5

156

67

28

5.7

Kiribati

0.11

31

..

..

..

..

..

14

10

42.7

Korea, Dem. Rep.

2011

..

..

..

..

..

..

749

4

0

..

Korea, Rep.

1.83

7

89.1

102.7

15.6

2.8

9,744

109

84

25.7

Kosovo

0.92

52

..

20.8

..

..

2,650

..

..

..

Kuwait

..

32

57.1

50.0

0.7

3.2

18,320

175

74

0.5 3.0

Kyrgyz Republic

0.95

10

2.7

..

16.1

4.2

1,375

116

20

Lao PDR

0.10

92

..

26.5

13.6

0.2

..

87

9

..

11.18

16

3.8

79.3

13.3

1.0

3,026

103

72

8.2

Lebanon

..

9

25.3

173.7

17.0 b

4.4

3,569

79

52

2.4

Lesotho

1.22

24

..

0.7

58.9

2.4

..

56

4

0.3

Liberia

..

6

..

30.9

0.2

0.7

..

49

3

..

Libya

..

..

..

–65.9

..

1.2

4,270

156

17

..

25.11

..

..

..

..

..

..

102

85

..

2.18

20

9.5

57.5

1.0

3,271

151

65

10.3

Luxembourg

7.31

19

114.2

172.4

24.3

..

16,834

148

91

9.7

Macedonia, FYR

4.12

2

24.0

45.5

19.1

1.3

3,591

107

57

3.9

Madagascar

0.08

8

..

11.7

13.0 b

0.7

..

41

2

8.4

Malawi

0.08

39

24.6

38.0

..

..

..

26

3

3.2

Malaysia

2.42

6

137.2

128.7

15.3

1.6

4,117

127

61

43.4

Maldives

3.09

9

..

82.5

11.0

..

..

166

34

..

..

8

..

16.6

14.6 b

1.8

..

68

2

2.4

Latvia

Liechtenstein Lithuania

Mali Malta

13.4b

9.52

40

38.5

159.2

27.7

0.7

4,151

125

69

47.2

Marshall Islands

..

17

..

..

..

..

..

..

4

..

Mauritania

..

19

..

42.4

..

3.8

..

94

5

..

Mauritius

7.88

6

58.1

110.0

18.4

0.1

..

99

35

0.8

Mexico

0.87

9

35.4

45.5

..

0.5

1,990

82

36

16.5

..

16

..

–18.3

..

..

..

25

20

..

Moldova

1.32

9

..

39.5

18.3

0.3

1,049

105

38

6.3

Monaco

..

..

..

..

..

..

..

90

75

..

Mongolia

..

12

18.0

40.3

21.9

0.9

1,530

105

20

..

10.44

10

73.9

61.8

..

2.0

5,547

185

40

..

1.28

12

60.0

110.9

3.3

781

113

51

7.7

Mozambique

..

13

..

25.0

..

0.9

444

33

4

26.5

Myanmar

..

..

..

..

..

..

131

3

1

0.0

Namibia

..

66

9.2

50.9

..

3.4

1,479

96

12

1.6

..

29

24.0

66.6

13.2

1.4

93

44

9

0.3

3.20

5

71.1

211.4

21.7b

1.4

7,010

115

92

19.8 15.4

Micronesia, Fed. Sts.

Montenegro Morocco

Nepal Netherlands New Caledonia New Zealand

23.6b

..

..

..

..

..

..

89

50

14.53

1

44.9

155.8

27.5b

1.1

9,566

109

86

9.3

..

39

..

46.1

15.2

0.6

473

82

11

5.3

0.00

17

..

14.7

..

0.9

..

30

1

4.5

Nicaragua Niger

84 

ages 15–64

World Development Indicators 2013

Front

?

..

User guide

World view

People

Environment


States and markets  5 Business Time Stock market Domestic Tax revenue Military Electric Mobile Individuals High-technology entry required capitalization credit collected expenditures power cellular using the exports density to start a provided by central consumption subscriptionsa Interneta by banking government per capita per 1,000 business sector people

Nigeria Northern Mariana Islands Norway

ages 15–64

days

% of GDP

% of GDP

% of GDP

% of GDP

kilowatt-hours

per 100 people

2010

2011

% of % of manufactured population exports

2011

June 2012

2011

2011

2011

2011

2011

2011

0.83

34

16.1

37.5

0.3

1.0

136

59

28

1.1

..

..

..

..

..

..

..

..

..

..

4.94

7

45.1

..

1.6

25,175

116

94

18.5

28.4b

Oman

1.67

8

27.5

32.3

2.2

6.0

5,933

169

68

2.6

Pakistan

0.03

21

15.6

43.3

9.3

3.0

457

62

9

1.8

..

28

..

..

..

..

..

75

..

..

0.08

7

39.9

104.9

..

..

1,832

189

43

35.4

Palau Panama Papua New Guinea

..

51

69.6

27.8

..

0.5

..

34

2

..

Paraguay

..

35

4.0

34.7

13.2

1.1

1,134

99

24

7.3

Peru

2.54

26

44.8

18.7

15.9

1.2

1,106

110

37

6.2

Philippines

0.19

36

73.6

51.8

12.3

1.1

643

99

29

46.4

Poland

0.52

32

26.9

66.2

17.0 b

1.9

3,783

131

65

5.9

Portugal

3.92

5

26.0

204.1

21.5b

2.0

4,929

115

55

3.5

Puerto Rico

..

6

..

..

..

..

..

83

48

..

Qatar

..

9

72.5

70.2

14.4

2.2

14,997

123

86

0.0

Romania

4.41

10

11.2

52.1

17.2b

1.1

2,392

109

44

10.2

Russian Federation

0.83

18

42.9

39.5

15.4b

3.9

6,431

179

49

8.0

Rwanda

0.78

3

..

..

..

1.2

..

41

7

3.4

Samoa

1.37

9

..

47.3

..

..

..

91

7

0.2

San Marino São Tomé and Príncipe Saudi Arabia

..

..

..

..

22.3

..

..

112

50

..

2.56

7

..

39.5

..

..

..

68

20

14.0

..

21

58.7

–4.8

..

8.4

7,967

191

48

0.7

Senegal

0.19

5

..

31.5

..

1.6

195

73

18

0.6

Serbia

1.66

12

18.3

54.1

20.5

2.1

4,359

125

42

..

..

39

..

45.8

31.7

0.9

..

146

43

3.4

Sierra Leone

0.38

12

..

12.9

11.1

0.9

..

36

0

..

Singapore

8.45

3

128.6

93.6

14.1

3.6

8,307

150

71

45.2

Seychelles

Sint Maarten

..

..

..

..

..

..

..

..

..

Slovak Republic

4.81

16

4.9

54.1

12.7b

..

1.1

5,164

109

74

7.1

Slovenia

4.04

6

12.8

94.7

17.9

1.4

6,521

107

72

5.9

Solomon Islands

..

9

..

15.0

..

..

..

50

6

..

Somalia

..

..

..

..

..

..

..

7

1

..

0.77

19

209.6

175.0

1.3

4,803

127

21

5.1

South Africa South Sudan

0.31

..

..

..

Spain

2.59

28

69.8

230.9

Sri Lanka

0.58

7

32.8

46.2

St. Kitts and Nevis

5.69

19

85.8

123.3

St. Lucia

3.00

15

..

114.6

25.7b ..

2.9

..

..

..

..

9.5b

1.0

6,155

113

68

6.4

12.4

2.6

449

87

15

1.0

19.7

..

..

153

76

0.1

..

..

..

123

42

16.9

St. Martin

..

..

..

..

..

..

..

..

..

..

St. Vincent and Grenadines

..

10

..

56.6

22.2

..

..

121

43

0.0

Sudan

..

36

..

23.0

..

..

141

56

19

29.4

Suriname

1.02

694

..

24.1

..

..

..

179

32

6.5

Swaziland

..

56

..

25.8

..

3.0

..

64

18

..

Sweden

7.17

16

87.1

142.3

21.9b

1.3

14,939

119

91

13.3

Switzerland

2.52

18

141.4

185.1

10.4

0.8

8,175

131

85

24.4

Syrian Arab Republic

0.05

13

..

47.7

..

3.9

1,905

63

23

1.3

Tajikistan

0.29

24

..

..

..

..

2,004

91

13

..

Economy

States and markets

Global links

Back

World Development Indicators 2013 85


5  States and markets Business Time Stock market Domestic Tax revenue Military Electric Mobile Individuals High-technology entry required capitalization credit collected expenditures power cellular using the exports density to start a provided by central consumption subscriptionsa Interneta by banking government per capita per 1,000 business sector people ages 15–64

days

% of GDP

2011

June 2012

% of GDP

% of GDP

% of GDP

kilowatt-hours

per 100 people

2011

2010

2011

2011

2011

2011

Tanzania

..

26

6.4

24.2

..

Thailand

0.59

29

77.7

159.0

Timor-Leste

17.6b

2011

2011

1.1

78

56

12

5.4

1.6

2,243

112

24

20.7

2.6

..

53

1

..

1.6

107

50

4

0.2 0.0

..

94

..

–26.5

Togo

0.11

38

..

35.4

Tonga

1.96

16

..

29.0

..

..

..

53

25

..

41

65.5

32.3

26.2

..

5,894

136

55

0.1

Tunisia

0.63

11

20.8

82.1

20.9

1.3

1,350

117

39

5.6

Turkey

20.1b

1.8

Trinidad and Tobago

..

% of % of manufactured population exports

17.1b

0.96

6

26.0

69.3

2.3

2,477

89

42

Turkmenistan

..

..

..

..

..

..

2,403

69

5

..

Turks and Caicos Islands

..

..

..

..

..

..

..

..

..

2.1

Tuvalu

..

..

..

..

..

..

..

22

30

..

0.72

33

46.0

18.7

16.1

1.6

..

48

13

21.9

Ukraine

0.60

22

15.5

73.4

18.3

2.5

3,550

123

31

4.4

United Arab Emirates

1.37

8

26.0

81.4

..

5.4

11,044

149

70

3.2

10.41

13

118.7

212.6

27.4b

2.6

5,733

131

82

21.3

..

6

104.3

234.9

10.1b

4.7

13,394

93

78

18.1

Uruguay

3.36

7

0.4

29.9

19.6

1.9

2,763

141

51

5.8

Uzbekistan

0.82

12

..

..

..

..

1,648

92

30

..

Vanuatu

2.18

35

..

70.5

..

..

..

56

8

..

Venezuela, RB

..

144

1.6

29.3

..

0.8

3,287

98

40

2.5

Vietnam

..

34

14.8

120.7

..

2.2

1,035

143

35

8.6

Virgin Islands (U.S.)

..

..

..

..

..

..

..

..

27

..

West Bank and Gaza

..

48

..

..

..

..

..

46

41

..

Yemen, Rep.

..

40

..

22.7

..

4.4

249

47

15

0.3 24.8

Uganda

United Kingdom United States

Zambia Zimbabwe World

1.26

17

20.9

18.0

16.6

1.6

623

61

12

..

90

112.9

..

..

1.6

1,022

72

16

14.8 w

2.5 w

2,975

85 w

33 w

3.42 u

30 u

68.7 w

164.9 w

w

1.0 17.6 w

Low income

0.32

30

..

40.4

11.6

1.6

242

42

6

..

Middle income

2.30

35

47.7

92.4

13.2

2.0

1,823

86

27

17.1

Lower middle income

1.01

31

43.2

58.9

11.9

2.0

698

80

16

9.3

Upper middle income

3.50

40

48.8

101.3

13.6

2.0

2,942

92

38

18.6

Low & middle income

2.01

34

47.4

91.6

13.2

2.0

1,661

80

24

17.0

East Asia & Pacific

1.04

37d

50.6

132.5

10.9

1.8

2,337

81

34

26.0

Europe & Central Asia

2.82

15d

32.9

49.2

17.1

3.0

4,059

132

42

6.3

Latin America & Carib.

2.84

58d

42.0

68.5

..

1.3

1,973

107

39

10.9

Middle East & N. Africa

0.36

25d

..

53.6

23.8

3.5

1,658

89

27

2.9

South Asia

0.16

19d

48.2

69.7

10.3

2.5

555

69

9

6.4

Sub-­Saharan Africa

1.99

32d

..

74.7

..

1.5

553

53

13

2.8

High income

6.38

18

78.6

203.2

14.6

2.8

9,415

114

76

17.9

Euro area

5.10

13

41.9

153.5

17.6

1.5

6,847

125

73

14.9

a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication/ICT Indicators database. Please cite ITU for third party use of these data. b. Data were reported on a cash basis and have been adjusted to the accrual framework of the International Monetary Fund’s Government Finance Statistics Manual 2001. c. Differs from the official value published by the government of China (1.3 percent; see National Bureau of Statistics of China, www.stats.gov.cn). d. Differs from data reported on the Doing Business website because the regional aggregates on the Doing Business website include developed economies.

86 

World Development Indicators 2013

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User guide

World view

People

Environment


States and markets  5 About the data

Entrepreneurial activity

different estimates, the Doing Business time indicators represent

The rate new businesses are added to an economy is a measure of

the median values of several responses given under the assumptions

its dynamism and entrepreneurial activity. Data on business entry

of the standardized case. Fifth, the methodology assumes that a

density are from the World Bank’s 2012 Entrepreneurship Data-

business has full information on what is required and does not waste

base, which includes indicators for more than 150 countries for

time when completing procedures. In constructing the indicators, it is

2004–11. Survey data are used to analyze firm creation, its relation-

assumed that entrepreneurs know about all regulations and comply

ship to economic growth and poverty reduction, and the impact of

with them. In practice, entrepreneurs may not be aware of all required

regulatory and institutional reforms. Data on total registered busi-

procedures or may avoid legally required procedures altogether.

nesses were collected from national registrars of companies. For cross-country comparability, only limited liability corporations that

Financial systems

operate in the formal sector are included. For additional information

Stock markets and banking systems both enhance growth, the main

on sources, methodology, calculation of entrepreneurship rates,

factor in poverty reduction. At low levels of economic development com-

and data limitations see http://www.doingbusiness.org/data/

mercial banks tend to dominate the financial system, while at higher

exploretopics/entrepreneurship.

levels domestic stock markets become more active and efficient.

Data on time required to start a business are from the Doing Busi-

Open economies with sound macroeconomic policies, good legal

ness database, whose indicators measure business regulation, gauge

systems, and shareholder protection attract capital and thus have

regulatory outcomes, and measure the extent of legal protection of

larger financial markets. The table includes market capitalization

property, the flexibility of employment regulation, and the tax burden

as a share of gross domestic product (GDP) as a measure of stock

on businesses. The fundamental premise is that economic activity

market size. Market size can be measured in other ways that may

requires good rules and regulations that are efficient, accessible,

produce a different ranking of countries. Recent research on stock

and easy to implement. Some indicators give a higher score for more

market development shows that modern communications tech-

regulation, such as stricter disclosure requirements in related-party

nology and increased financial integration have resulted in more

transactions, and others give a higher score for simplified regulations,

cross-border capital flows, a stronger presence of financial firms

such as a one-stop shop for completing business startup formalities.

around the world, and the migration of trading activities to interna-

There are 11 sets of indicators covering starting a business, register-

tional exchanges. Many firms in emerging markets now cross-list

ing property, dealing with construction permits, getting electricity,

on international exchanges, which provides them with lower cost

enforcing contracts, getting credit, protecting investors, paying taxes,

capital and more liquidity-traded shares. However, this also means

trading across borders, resolving insolvency, and employing workers.

that exchanges in emerging markets may not have enough financial

The indicators are available at www.doingbusiness.org.

activity to sustain them. Comparability across countries may be lim-

Doing Business data are collected with a standardized survey that uses a simple business case to ensure comparability across

ited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards.

economies and over time—with assumptions about the legal form

Standard & Poor’s (S&P) Indices provides regular updates on 21

of the business, its size, its location, and nature of its operation.

emerging stock markets and 36 frontier markets. The S&P Global

Surveys in 185 countries are administered through more than 9,000

Equity Indices, S&P Indices’s leading emerging markets index, is

local experts, including lawyers, business consultants, accountants,

designed to be sufficiently investable to support index tracking portfo-

freight forwarders, government officials, and other professionals who

lios in emerging market stocks that are legally and practically open to

routinely administer or advise on legal and regulatory requirements.

foreign portfolio investment. The S&P Frontier Broad Market Index mea-

The Doing Business methodology has limitations that should be

sures the performance of 36 smaller and less liquid markets. These

considered when interpreting the data. First, the data collected refer

indexes are widely used benchmarks for international portfolio manage-

to businesses in the economy’s largest city and may not represent

ment. See www.spindices.com for further information on the indexes.

regulations in other locations of the economy. To address this limita-

Because markets included in S&P’s emerging markets category

tion, subnational indicators are being collected for selected econo-

vary widely in level of development, it is best to look at the entire

mies; they point to significant differences in the speed of reform

category to identify the most significant market trends. And it is

and the ease of doing business across cities in the same economy.

useful to remember that stock market trends may be distorted by

Second, the data often focus on a specific business form—generally

currency conversions, especially when a currency has registered

a limited liability company of a specified size—and may not represent

a significant devaluation (Demirgüç-Kunt and Levine 2006; Beck

regulation for other types of businesses such as sole proprietor-

and Levine 2001; and Claessens, Klingebiel, and Schmukler 2002).

ships. Third, transactions described in a standardized business case

Domestic credit provided by the banking sector as a share of GDP

refer to a specific set of issues and may not represent all the issues

measures banking sector depth and financial sector development in

a business encounters. Fourth, the time measures involve an ele-

terms of size. Data are taken from the banking survey of the Interna-

ment of judgment by the expert respondents. When sources indicate

tional Monetary Fund’s (IMF) International Financial Statistics or, when

Economy

States and markets

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World Development Indicators 2013 87


5  States and markets unavailable, from its monetary survey. The monetary survey includes

data are not always comparable across countries. However, SIPRI puts

monetary authorities (the central bank), deposit money banks, and

a high priority on ensuring that the data series for each country is com-

other banking institutions, such as finance companies, development

parable over time. More information on SIPRI’s military expenditure

banks, and savings and loan institutions. In a few countries govern-

project can be found at www.sipri.org/research/armaments/milex.

ments may hold international reserves as deposits in the banking system rather than in the central bank. Claims on the central gov-

Infrastructure

ernment are a net item (claims on the central government minus

The quality of an economy’s infrastructure, including power and com-

central government deposits) and thus may be negative, resulting in

munications, is an important element in investment decisions and

a negative value for domestic credit provided by the banking sector.

economic development. The International Energy Agency (IEA) collects data on electric power consumption from national energy agencies

Tax revenues

and adjusts the values to meet international definitions. Consump-

Taxes are the main source of revenue for most governments. Tax

tion by auxiliary stations, losses in transformers that are considered

revenue as a share of GDP provides a quick overview of the fiscal

integral parts of those stations, and electricity produced by pumping

obligations and incentives facing the private sector across coun-

installations are included. Where data are available, electricity gen-

tries. The table shows only central government data, which may

erated by primary sources of energy—coal, oil, gas, nuclear, hydro,

significantly understate the total tax burden, particularly in countries

geothermal, wind, tide and wave, and combustible renewables—are

where provincial and municipal governments are large or have con-

included. Consumption data do not capture the reliability of supplies,

siderable tax authority.

including breakdowns, load factors, and frequency of outages.

Low ratios of tax revenue to GDP may reflect weak administration and

The International Telecommunication Union (ITU) estimates that

large-scale tax avoidance or evasion. Low ratios may also reflect a

there were 5.9 billion mobile subscriptions globally in 2011. No

sizable parallel economy with unrecorded and undisclosed incomes.

technology has ever spread faster around the world. Mobile com-

Tax revenue ratios tend to rise with income, with higher income coun-

munications have a particularly important impact in rural areas.

tries relying on taxes to finance a much broader range of social

The mobility, ease of use, flexible deployment, and relatively low

services and social security than lower income countries are able to.


and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The

Military expenditures

next billion mobile subscribers will consist mainly of the rural poor.

Although national defense is an important function of government,

Operating companies have traditionally been the main source of

high expenditures for defense or civil conflicts burden the economy

telecommunications data, so information on subscriptions has been

and may impede growth. Military expenditures as a share of GDP

widely available for most countries. This gives a general idea of access,

are a rough indicator of the portion of national resources used for

but a more precise measure is the penetration rate—the share of

military activities. As an “input” measure, military expenditures are

households with access to telecommunications. During the past few

not directly related to the “output” of military activities, capabilities,

years more information on information and communication technology

or security. Comparisons across countries should take into account

use has become available from household and business surveys. Also

many factors, including historical and cultural traditions, the length

important are data on actual use of telecommunications services. The

of borders that need defending, the quality of relations with neigh-

quality of data varies among reporting countries as a result of differ-

bors, and the role of the armed forces in the body politic.

ences in regulations covering data provision and availability.

Data are from the Stockholm International Peace Research Institute

88 

(SIPRI), whose primary source of military expenditure data is offi-

High-technology exports

cial data provided by national governments. These data are derived

The method for determining high-technology exports was developed

from budget documents, defense white papers, and other public

by the Organisation for Economic Co-operation and Development in

documents from official government agencies, including govern-

collaboration with Eurostat. It takes a “product approach” (rather than

ment responses to questionnaires sent by SIPRI, the United Nations

a “sectoral approach”) based on research and development intensity

Office for Disarmament Affairs, or the Organization for Security and

(expenditure divided by total sales) for groups of products from Ger-

­Co-operation in Europe. Secondary sources include international sta-

many, Italy, Japan, the Netherlands, Sweden, and the United States.

tistics, such as those of the North Atlantic Treaty Organization (NATO)

Because industrial sectors specializing in a few high-technology prod-

and the IMF’s Government Finance Statistics Yearbook. Other second-

ucts may also produce low-technology products, the product approach

ary sources include country reports of the Economist Intelligence Unit,

is more appropriate for international trade. The method takes only

country reports by IMF staff, and specialist journals and newspapers.

research and development intensity into account, but other characteris-

In the many cases where SIPRI cannot make independent estimates,

tics of high technology are also important, such as knowhow, scientific

it uses country-provided data. Because of differences in definitions

personnel, and technology embodied in patents. Considering these

and the difficulty of verifying the accuracy and completeness of data,

characteristics would yield a different list (see Hatzichronoglou 1997).

World Development Indicators 2013

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User guide

World view People view People Environment


States and markets  5 Definitions

telepoint, radio paging, and telemetry services. • Individuals using

• Business entry density is the number of newly registered lim-

the Internet are the percentage of individuals who have used the

ited liability corporations per 1,000 people ages 15–64. • Time

Internet (from any location) in the last 12 months. Internet can

required to start a business is the number of calendar days to com-

be used via a computer, mobile phone, personal digital assistant,

plete the procedures for legally operating a business using the fast-

games machine, digital television, or similar device. • High-tech-

est procedure, independent of cost. • Stock market capitalization

nology exports are products with high research and development

(also known as market value) is the share price times the number of

intensity, such as in aerospace, computers, pharmaceuticals, sci-

shares outstanding. • Domestic credit provided by banking sector

entific instruments, and electrical machinery.

is all credit to various sectors on a gross basis, except to the central government, which is net. The banking sector includes monetary

Data sources

authorities, deposit money banks, and other banking institutions

Data on business entry density are from the World Bank’s Entrepre-

for which data are available. • Tax revenue collected by central

neurship Database (www.doingbusiness.org/data/exploretopics/

government is compulsory transfers to the central government for

entrepreneurship). Data on time required to start a business are

public purposes. Certain compulsory transfers such as fines, penal-

from the World Bank’s Doing Business project (www.doingbusiness

ties, and most social security contributions are excluded. Refunds

.org). Data on stock market capitalization are from Standard &

and corrections of erroneously collected tax revenue are treated as

Poor’s (2012). Data on domestic credit are from the IMF’s Inter-

negative revenue. The analytic framework of the IMF’s Government

national Financial Statistics. Data on central government tax rev-

Finance Statistics Manual 2001 (GFSM 2001) is based on accrual

enue are from the IMF’s Government Finance Statistics. Data on

accounting and balance sheets. For countries still reporting govern-

military expenditures are from SIPRI’s Military Expenditure Database

ment finance data on a cash basis, the IMF adjusts reported data to

(www.sipri.org/databases/milex). Data on electricity consumption

the GFSM 2001 accrual framework. These countries are footnoted

are from the IEA’s Energy Statistics of Non-OECD Countries, Energy

in the table. • Military expenditures are SIPRI data derived from

Balances of Non-OECD Countries, and Energy Statistics of OECD

NATO’s former definition (in use until 2002), which includes all cur-

Countries and from the United Nations Statistics Division’s Energy

rent and capital expenditures on the armed forces, including peace-

Statistics Yearbook. Data on mobile cellular phone subscriptions

keeping forces; defense ministries and other government agencies

and individuals using the Internet are from the ITU’s World Tele-

engaged in defense projects; paramilitary forces, if judged to be

communication/ICT Indicators database and TeleGeography. Data

trained and equipped for military operations; and military space

on high-technology exports are from the United Nations Statistics

activities. Such expenditures include military and civil personnel,

Division’s Commodity Trade (Comtrade) database.

including retirement pensions and social services for military personnel; operation and maintenance; procurement; military research

References

and development; and military aid (in the military expenditures of

Beck, Thorsten, and Ross Levine. 2001. “Stock Markets, Banks, and

the donor country). Excluded are civil defense and current expendi-

Growth: Correlation or Causality?” Policy Research Working Paper

tures for previous military activities, such as for veterans benefits,

2670, World Bank, Washington, DC.

demobilization, and weapons conversion and destruction. This defi-

Claessens, Stijn, Daniela Klingebiel, and Sergio L. Schmukler. 2002.

nition cannot be applied for all countries, however, since that would

“Explaining the Migration of Stocks from Exchanges in Emerging

require more detailed information than is available about military

Economies to International Centers.” Policy Research Working Paper

budgets and off-budget military expenditures (for example, whether

2816, World Bank, Washington, DC.

military budgets cover civil defense, reserves and auxiliary forces,

Demirgüç-Kunt, Asli, and Ross Levine. 1996. “Stock Market Devel-

police and paramilitary forces, and military pensions). • Electric

opment and Financial Intermediaries: Stylized Facts.” World Bank

power consumption per capita is the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power

Economic Review 10 (2): 291–321. Geneva Declaration on Armed Violence and Development. 2011. Global Burden of Armed Violence. Geneva.

plants, divided by midyear population. • Mobile cellular subscrip-

Hatzichronoglou, Thomas. 1997. “Revision of the High-Technology

tions are the number of subscriptions to a public mobile telephone

Sector and Product Classification.” STI Working Paper 1997/2.

service that provides access to the public switched telephone net-

Organisation for Economic Co-operation and Development, Direc-

work using cellular technology. Postpaid subscriptions and active

torate for Science, Technology, and Industry, Paris.

prepaid accounts (that is, accounts that have been used during

Standard & Poors. 2012. Global Stock Markets Factbook 2012. New York.

the last three months) are included. The indicator applies to all

WIPO (World Intellectual Property Organization). 2012. World Intel-

mobile cellular subscriptions that offer voice communications and excludes subscriptions for data cards or USB modems, subscriptions to public mobile data services, private-trunked mobile radio,

Economy

States and markets

lectual Property Indicators 2012. Geneva. World Bank. 2012. CPIA Africa: Accessing Africa’s Policies and Institutions. Washington, DC.

Global links

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World Development Indicators 2013 89


5  States and markets To access the World Development Indicators online tables, use

indicator online, use the URL http://data.worldbank.org/indicator/

the URL http://wdi.worldbank.org/table/ and the table number (for

and the indicator code (for example, http://data.worldbank.org/

example, http://wdi.worldbank.org/table/5.1). To view a specific

indicator/IE.PPI.TELE.CD).

5.1 Private sector in the economy

S&P/Global Equity Indices IE.PPI.TELE.CD

Energy investment

IE.PPI.ENGY.CD

5.5 Financial access, stability, and efficiency

Transport investment

IE.PPI.TRAN.CD

Strength of legal rights index

Water and sanitation investment

IE.PPI.WATR.CD

Depth of credit information index

Domestic credit to private sector

FS.AST.PRVT.GD.ZS

Depositors with commercial banks

FB.CBK.DPTR.P3

IC.BUS.NREG

Borrowers from commercial banks

FB.CBK.BRWR.P3

Commercial bank branches

FB.CBK.BRCH.P5

Businesses registered, New Businesses registered, Entry density

IC.BUS.NDNS.ZS

5.2 Business environment: enterprise surveys Time dealing with officials

IC.GOV.DURS.ZS

Average number of times meeting with tax officials

IC.TAX.METG

Time required to obtain operating license

IC.FRM.DURS

Informal payments to public officials

IC.FRM.CORR.ZS

Losses due to theft, robbery, vandalism, and arson

IC.FRM.CRIM.ZS

IC.LGL.CRED.XQ IC.CRD.INFO.XQ

Automated teller machines

FB.ATM.TOTL.P5

Bank capital to assets ratio

FB.BNK.CAPA.ZS

Ratio of bank non-performing loans to total gross loans

FB.AST.NPER.ZS

Domestic credit provided by banking sector

FS.AST.DOMS.GD.ZS

Interest rate spread

FR.INR.LNDP

Risk premium on lending

FR.INR.RISK

5.6 Tax policies

Firms competing against unregistered firms

IC.FRM.CMPU.ZS

Tax revenue collected by central government

Firms with female top manager

IC.FRM.FEMM.ZS

Number of tax payments by businesses

Firms using banks to finance investment

IC.FRM.BNKS.ZS

Value lost due to electrical outages

IC.FRM.OUTG.ZS

Time for businesses to prepare, file and pay taxes Business profit tax

GC.TAX.TOTL.GD.ZS IC.TAX.PAYM IC.TAX.DURS IC.TAX.PRFT.CP.ZS

Internationally recognized quality certification ownership

IC.FRM.ISOC.ZS

Business labor tax and contributions

IC.TAX.LABR.CP.ZS

Average time to clear exports through customs

IC.CUS.DURS.EX

Other business taxes

IC.TAX.OTHR.CP.ZS

Firms offering formal training

IC.FRM.TRNG.ZS

Total business tax rate

IC.TAX.TOTL.CP.ZS

5.3 Business environment: Doing Business indicators

5.7 Military expenditures and arms transfers

Number of procedures to start a business

IC.REG.PROC

Military expenditure, % of GDP

Time required to start a business

IC.REG.DURS

Military expenditure, % of central government expenditure

Cost to start a business

IC.REG.COST.PC.ZS

Number of procedures to register property

IC.PRP.PROC

Arm forces personnel

Time required to register property

IC.PRP.DURS

Arm forces personnel, % of total labor force

Number of procedures to build a warehouse Time required to build a warehouse Time required to get electricity

MS.MIL.XPRT.KD

IC.WRH.DURS

MS.MIL.MPRT.KD

Time required to enforce a contract

IC.LGL.DURS IC.BUS.DISC.XQ IC.ISV.DURS

5.4 Stock markets CM.MKT.LCAP.CD

Market capitalization, % of GDP

CM.MKT.LCAP.GD.ZS

Value of shares traded

CM.MKT.TRAD.GD.ZS

Turnover ratio

CM.MKT.TRNR

Listed domestic companies

World Development Indicators 2013

CM.MKT.LDOM.NO

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MS.MIL.TOTL.P1 MS.MIL.TOTL.TF.ZS

Arms transfers, Imports

IC.ELC.TIME

Market capitalization, $

MS.MIL.XPND.ZS

Arms transfers, Exports

IC.LGL.PROC

Time required to resolve insolvency

MS.MIL.XPND.GD.ZS

IC.WRH.PROC

Number of procedures to enforce a contract Business disclosure index

90 

CM.MKT.INDX.ZG

Telecommunications investment

User guide

5.8 Fragile situations International Development Association Resource Allocation Index Peacekeeping troops, police, and military observers

IQ.CPA.IRAI.XQ VC.PKP.TOTL.UN

Battle related deaths

VC.BTL.DETH

Intentional homicides

VC.IHR.PSRC.P5

Military expenditures

MS.MIL.XPND.GD.ZS

Losses due to theft, robbery, vandalism, and arson

IC.FRM.CRIM.ZS

Firms formally registered when operations started

IC.FRM.FREG.ZS

World view People view People Environment


States and markets  5 Children in employment

SL.TLF.0714.ZS

Refugees, By country of origin

SM.POP.REFG.OR

Refugees, By country of asylum

SM.POP.REFG

Internally displaced persons

VC.IDP.TOTL.HE

Access to an improved water source

SH.H2O.SAFE.ZS

Access to improved sanitation facilities

SH.STA.ACSN

Maternal mortality ratio, National estimate

SH.STA.MMRT.NE

Maternal mortality ratio, Modeled estimate

SH.STA.MMRT

Under-five mortality rate

SH.DYN.MORT

Depth of food deficit

SN.ITK.DFCT

Primary gross enrollment ratio

SE.PRM.ENRR

5.9 Public policies and institutions International Development Association Resource Allocation Index Macroeconomic management

IQ.CPA.IRAI.XQ IQ.CPA.MACR.XQ

Fiscal policy

IQ.CPA.FISP.XQ

Air freight

IS.AIR.GOOD.MT.K1

5.11 Power and communications Electric power consumption per capita Electric power transmission and distribution losses

EG.USE.ELEC.KH.PC EG.ELC.LOSS.ZS

Fixed telephone subscriptions

IT.MLT.MAIN.P2

Mobile cellular subscriptions

IT.CEL.SETS.P2

Fixed telephone international voice traffic

..a

Mobile cellular network international voice traffic

..a

Population covered by mobile cellular network

..a

Fixed telephone sub-basket

..a

Mobile cellular sub-basket

..a

Telecommunications revenue

..a

Mobile cellular and fixed-line subscribers per employee

..a

Debt policy

IQ.CPA.DEBT.XQ

5.12 The information age

Economic management, Average

IQ.CPA.ECON.XQ

Households with television

..a

Trade

IQ.CPA.TRAD.XQ

Households with a computer

..a

Financial sector

IQ.CPA.FINS.XQ

Individuals using the Internet

..a

Business regulatory environment

IQ.CPA.BREG.XQ

Structural policies, Average

IQ.CPA.STRC.XQ

Fixed (wired) broadband Internet subscriptions

Gender equality

IQ.CPA.GNDR.XQ

International Internet bandwidth

Equity of public resource use

IQ.CPA.PRES.XQ

Fixed broadband sub-basket

Building human resources

IQ.CPA.HRES.XQ

Secure Internet servers

Social protection and labor

IQ.CPA.PROT.XQ

Policies and institutions for environmental sustainability

Information and communications technology goods, Exports

TX.VAL.ICTG.ZS.UN

IQ.CPA.ENVR.XQ

Policies for social inclusion and equity, Average

IQ.CPA.SOCI.XQ

Information and communications technology goods, Imports

TM.VAL.ICTG.ZS.UN

Property rights and rule-based governance

IQ.CPA.PROP.XQ

Quality of budgetary and financial management

IQ.CPA.FINQ.XQ

Information and communications technology services, Exports

BX.GSR.CCIS.ZS

IT.NET.BBND.P2 ..a ..a IT.NET.SECR.P6

Efficiency of revenue mobilization

IQ.CPA.REVN.XQ

5.13 Science and technology

Quality of public administration

IQ.CPA.PADM.XQ

Research and development (R&D), Researchers

SP.POP.SCIE.RD.P6

Transparency, accountability, and corruption in the public sector

Research and development (R&D), Technicians

SP.POP.TECH.RD.P6

IQ.CPA.TRAN.XQ

Public sector management and institutions, Average

IQ.CPA.PUBS.XQ

5.10 Transport services Total road network

IS.ROD.TOTL.KM

Paved roads

IS.ROD.PAVE.ZS

Road passengers carried

IS.ROD.PSGR.K6

Road goods hauled

IS.ROD.GOOD.MT.K6

Scientific and technical journal articles Expenditures for R&D

IP.JRN.ARTC.SC GB.XPD.RSDV.GD.ZS

High-technology exports, $

TX.VAL.TECH.CD

High-technology exports, % of manufactured exports

TX.VAL.TECH.MF.ZS

Charges for the use of intellectual property, Receipts

BX.GSR.ROYL.CD

Charges for the use of intellectual property, Payments

BM.GSR.ROYL.CD

Rail lines

IS.RRS.TOTL.KM

Patent applications filed, Residents

IP.PAT.RESD

Railway passengers carried

IS.RRS.PASG.KM

Patent applications filed, Nonresidents

IP.PAT.NRES

Trademark applications filed, Total

IP.TMK.TOTL

Railway goods hauled

IS.RRS.GOOD.MT.K6

Port container traffic

IS.SHP.GOOD.TU

Registered air carrier departures worldwide

IS.AIR.DPRT

Air passengers carried

IS.AIR.PSGR

Economy

States and markets

Data disaggregated by sex are available in the World Development Indicators database. a. Available online only as part of the table, not as an individual indicator.

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World Development Indicators 2013 91


GLOBAL LINKS

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World Development Indicators 2013

Front

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User guide

World view

People

Environment


The world economy is bound together by trade in goods and services, financial flows, and the movement of people. As national economies develop, their links expand and grow more complex. The indicators in this section measure the size and direction of these flows and document the effects of policy interventions and aid flows on the world economy. The optimistic economic momentum at the beginning of 2011 slowed over the course of the year. The adverse effects of the tsunami in Japan coupled with intensification of the sovereign debt crisis in the euro area shook confidence at the global level. The slowdown became more pronounced in high-income economies, reducing the growth in capital inflows to developing countries. Net debt and equity inflows to developing economies in 2011 were $1.1 trillion—or 8 percent lower than in 2010 and below the level reached before the global financial crisis. The downturn was driven by the collapse of portfolio equity, again the most volatile of capital flows. Equity flows to emerging markets with good growth prospects, such as China, Brazil, and India, dropped substantially. Low- and middle-income economies recorded net inflows of only $8.3 billion in 2011, compared with $130 billion in 2010. Debt net inflows to developing countries in 2011 were $437 billion—down 9 percent from 2010. The slowdown was led by a drop in lending by official creditors from $77 billion in 2010 to $30 billion in 2011. However, commercial bank financing tripled to $110 billion, and the private sector created more liquidity through bond issuances, reaching their highest stock level of $1.5 trillion. Net inflows of short-term debt shrank 27 percent in 2011 after being the fastest growing debt component the previous year.

Economy

States and markets

Global foreign direct investment (FDI) rose 22.7 percent in 2011, to its pre-crisis level. Some 40 percent of those investments were directed to developing economies. In 2011 many developing countries continued to implement policy changes to further liberalize and facilitate FDI entry and operations and to regulate FDI. The largest recipients of FDI inflows were Brazil, China, India, and the Russian Federation, accounting for more than half of inflows to developing economies. China was the largest recipient, with net FDI inflows of $220 billion, a decline of 10 percent compared with 2010. In 2011 world merchandise exports to developing countries increased 27 percent from 2010, while exports to high-income countries increased 20 percent. Brazil, China, India, and the Russian Federation were among the top traders, with China accounting for 70 percent of East Asia and Pacific’s merchandise trade. Official development assistance, a stable source of development financing and buffer against the impact of several financial crises, was $134 billion in 2011, or 0.58 percent of developing countries’ combined gross national income, down from 0.65 percent in 2010. Worldwide tourism continues to grow and has become one of the largest and fastest growing economic sectors in the world. The number of international tourist arrivals in 2011 reached a record high of over 1 billion, and inbound tourism expenditures increased 12 percent to $1.3 trillion from 2010. The only developing region that saw a drop in tourist arrivals in 2011 was the Middle East and North Africa, due to political instability and armed conflict in the region.

Global links Back links Back

6

World Development Indicators 2013 93


Highlights East Asia & Pacific: Equity investment drops After recovering from a 2009 low by 2010, equity investment in East

Equity inflows to East Asia and Pacific ($ billions)

Asia and Pacific fell again in 2011. Turmoil in the euro area and the

300

natural disaster in Japan caused a large withdrawal of foreign equity.

Foreign direct investment

Foreign direct investment (FDI) net inflows combined with portfolio

250

equity inflows in 2011 were 17 percent lower than in 2010. Net FDI

200

flows declined 5 percent, while portfolio equity inflows fell to a fifth their 2010 level. China, the largest recipient of capital flows into the

150

region (with 80 percent), recorded a 10 percent decline of FDI inflows

100

compared with 2010 and portfolio equity inflows a sixth of their 2010 level. Investments in the service sector in China maintained their

50

growth rate.

0 –50

Portfolio equity 2000

2002

2004

2006

2008

2010 2011

Source: Online table 6.10.

Europe & Central Asia: Remittance flows diverge High unemployment in the developed countries of the European Union

Remittance inflows ($ billions)

impaired employment prospects of existing migrants and limited their

30

ability to send money home. Remittance flows to developing countries in Eastern Europe dropped sharply in 2009 and have yet to recover.

25

Commonwealth of Independent States

Romania, one of the largest recipients of remittances in the European Union, experienced an annual average decrease of 30 percent over

20

the last three years. In contrast, remittances to almost all the member 15

countries of the Commonwealth of Independent States rebounded Eastern Europe

10

strongly. Rising employment and higher real wages in the Russian Federation gave migrants the opportunity to increase their remittances. Tajikistan, with the most emigrants to the Russian Federation,

5

depends on remittances as a major source of external finance. In 2011 it received $3.2 billion in remittances, equivalent to 46 percent

0 2002

2004

2006

2008

2010

2011

of GDP.

Source: Online table 6.14.

Latin America & Caribbean: More trade with developing countries Trade between developing countries continues to grow. Between 2000

Share of total merchandise exports (%)

and 2010 merchandise exports between developing countries grew

80

Developing countries to high-income countries Brazil to high-income countries

more than 600 percent, while developing country exports to high-income countries grew half that amount. By 2011 a third of developing country trade went to other developing countries, while the share of merchan-

60

dise exports from developing countries to high-income countries fell 40

from 77 percent in 2000 to about 63 percent. In Latin America and the

Brazil to developing countries

Caribbean, Brazil is a leading example. Since 2009 Brazil’s merchandise Developing countries to developing countries

20

exports to developing countries have exceeded those to high-income countries—largely due to Brazil’s increased natural resource exports to Asia, particularly to China. In addition, intraregional trade has increased since 2009, especially between Common Market of the South members

0

(Argentina, Brazil, Paraguay, Uruguay, and Venezuela), surpassing the 2000

2002

2004

2006

2008

2010 2011

level reached before the global financial crisis.

Source: Online table 6.4.

94 

World Development Indicators 2013

Front

?

User guide

World view

People

Environment


Middle East & North Africa: Tourist arrivals fall due to instability Despite increased worldwide tourism, tourist arrivals fell in the Middle East and North Africa in 2011 due to political instability and armed conflict in the region (World Tourism Organization 2012b).

International tourist arrivals (millions) 80

“The Arab Spring,” as it has come to be known, began in Tunisia with mass demonstrations and riots at the end of 2010. As a

60

result, international tourist arrivals to Tunisia fell 31 percent, from 6.9 million in 2010 to 4.8 million in 2011, the lowest level since 1998, and tourist expenditures fell from $3.5 billion to $2.5 bil-

40

lion. In February 2011 Egypt’s government was overthrown. Egypt 20

in 2011.

2009

tourist expenditures fall from $51.2 billion in 2010 to $43.1 billion 0

2011

18 percent increase in 2010. Heavily dependent on tourism, it saw

2010

experienced a 32 percent drop in tourist arrivals in 2011 after an

Middle East & North Africa

Egypt

Tunisia

Source: Online table 6.15.

South Asia: Encouraging investment in China and India China and India, the two largest and fastest growing economies in the developing world, have altered their regulatory systems to attract and keep inflows of foreign direct investment (FDI). China started first. It began opening its economy in 1979 and continued through to its membership in the World Trade Organization in 2001, reassuring

Net inflows of foreign direct investment (% of GDP) 5 China

4

investors of its increasing reliability. While FDI in China was driven by export-oriented policies, India pursued an import substitution policy,

3

promoting domestic firms and limiting the rights of foreign investors. It was not until the 1990s that India started significantly liberalizing

2

FDI, and it only recently liberalized the FDI policies of its retail sector. Before the financial crisis net FDI inflows as a share of GDP to India

India 1

began to catch up with those to China. After bottoming out in 2010, they began to recover, while net FDI inflows as a share of GDP to China

0 2000

dropped again.

2002

2004

2006

2008

2010 2011

Source: Online table 6.10.

Sub-­Saharan Africa: Less official development assistance in 2011 Official development assistance to Sub-­Saharan Africa as a share of the region’s gross national income fell in 2011. But aid to three of the five largest recipients, the Democratic Republic of Congo, São Tomé

Net official development assistance from all donors (% of GNI) 150

and Príncipe, and Rwanda, rose. The Democratic Republic of Congo was second among all countries receiving aid from Development Assistance Committee (DAC) members (after Afghanistan). But this distinction is

100

likely to be short lived. Like Liberia in 2010, the Democratic Republic of Congo benefited from debt forgiveness after reaching the completion point under the enhanced Heavily Indebted Poor Countries initiative in

50

Congo, increasing their aid to the country from $278 million to $1.3 bil-

0 Rwanda

Burundi

lion and from $135 million to $1.1 billion respectively from 2010.

São Tomé and Príncipe

Congo, Dem. Rep.

2011

and France were also the top DAC donors to the Democratic Republic of

2009

the end of 2010. As its two major Paris Club creditors, the United States

2010

July 2010 and the subsequent Paris Club rescheduling agreement at

Liberia

Source: Online table 6.12.

Economy

States and markets

Global links Back links Back

World Development Indicators 2013 95


6  Global links Merchandise Net barter Inbound Net official Net Personal Foreign trade terms of tourism development migration transfers and direct trade index expenditure assistance compensation investment of employees, received

Total external debt stock

Total debt service

% of GDP

2000 = 100

% of exports

% of GNI

thousands

$ millions

Net inflow $ millions

Net inflow $ millions

$ millions

% of exports of goods, services, and primary incomea

2011

2011

2011

2011

2005–10

2011

2011

2011

2011

2011

Afghanistan

33.1

146.3

..

35.0

–381

..

83

..

2,623

..

Albania

56.6

94.1

41.7

2.4

–48

1,162

1,370

2

5,938

9.3

Algeria

63.5

199.1

0.4

0.1

–140

203

2,721

0

6,072

0.8

American Samoa

..

129.0

..

..

..

..

..

..

..

..

Andorra

..

..

..

..

..

..

..

..

..

..

82.9

244.7

1.0

0.2

82

0

–3,024

0

21,115

4.3

Angola Antigua and Barbuda

47.0

75.1

58.1

1.4

..

20

58

..

..

..

Argentina

35.5

135.2

6.2

0.0

–200

686

8,671

–174

114,704

15.3

Armenia

53.5

128.5

20.2

3.5

–75

1,994

663

0

7,383

25.4

..

118.5

19.8

..

4

5

544

0

..

..

Australia

37.3

200.7

..

..

1,125

1,871

67,638

–3,308

..

..

Austria

88.8

89.0

9.6

..

160

2,674

15,734

483

..

..

Azerbaijan

71.3

187.6

4.0

0.5

53

1,893

4,485

0

8,427

4.9

Bahamas, The

46.4

108.6

66.0

..

6

..

595

..

..

..

..

128.5

7.7

..

448

..

781

982

..

..

54.2

54.8

0.4

1.2

–2,908

12,068

798

–10

27,043

5.5

Aruba

Bahrain Bangladesh Barbados

62.7

111.0

..

..

0

82

334

..

..

..

Belarus

156.3

103.8

1.9

0.2

–50

814

4,002

0

29,120

4.5

Belgium

182.4

100.1

3.0

..

200

10,912

102,000

–4,200

..

..

Belize

86.4

104.4

26.7

2.3

–1

76

95

..

1,278

13.9

Benin

61.7

125.1

..

9.3

50

185

118

..

1,423

..

..

72.4

..

..

..

1,253

111

–3

..

..

Bhutan

90.6

151.5

..

8.7

17

10

16

0

1,035

11.1

Bolivia

66.8

175.4

5.5

3.3

–165

1,043

859

0

6,474

4.9

Bosnia and Herzegovina

93.4

101.5

9.8

2.3

–10

1,958

380

..

10,729

10.9

Bermuda

Botswana

74.7

82.4

..

0.7

19

63

587

..

2,396

..

Brazil

19.9

135.8

2.3

0.0

–500

2,798

71,539

7,174

404,317

19.4

Brunei Darussalam

94.7

200.6

..

..

4

..

1,208

..

..

..

112.3

110.7

12.7

..

–50

1,483

2,588

–42

39,930

12.2

Burkina Faso

42.3

141.2

..

9.5

–125

111

7

..

2,420

..

Burundi

36.1

172.6

1.6

24.8

370

45

3

..

628

3.4

Bulgaria

Cambodia

126.7

70.6

24.1

6.5

–255

160

902

0

4,336

1.0

Cameroon

44.0

149.6

..

2.5

–19

115

360

..

3,074

..

Canada

52.7

122.5

3.7

..

1,098

..

39,510

21,313

..

..

Cape Verde

53.7

106.0

55.5

13.3

–17

177

105

..

1,025

5.0

Cayman Islands

..

77.6

..

..

..

..

7,408

..

..

..

Central African Republic

24.6

82.4

..

12.4

5

..

109

..

573

..

Chad

64.3

208.8

..

4.9

–75

..

1,855

..

1,821

..

..

..

..

..

5

..

..

..

..

..

62.3

213.3

..

0.0

30

4

17,299

4,477

96,245

15.2

Channel Islands Chile China

49.8

72.8

2.6

0.0

–1,884

40,483

220,143

5,308

685,418

3.6

388.9

95.9

6.0

..

176

357

95,352

9,814

..

..

24.2

87.1

..

..

51

48

2,116

0

..

..

Colombia

33.5

142.4

4.9

0.4

–120

4,205

13,605

1,969

76,918

15.6

Comoros

49.5

76.1

..

8.5

–10

..

7

..

278

..

Congo, Dem. Rep.

77.3

146.7

..

38.4

–24

115

1,596

..

5,448

2.4

110.2

214.5

..

2.4

50

..

2,931

..

2,523

..

Hong Kong SAR, China Macao SAR, China

Congo, Rep.

96 

Portfolio equity

World Development Indicators 2013

Front

?

User guide

World view

People

Environment


Global links  6 Merchandise Net barter Inbound Net official Net Personal Foreign trade terms of tourism development migration transfers and direct trade index expenditure assistance compensation investment of employees, received

Portfolio equity

% of GDP

2000 = 100

% of exports

% of GNI

thousands

$ millions

Net inflow $ millions

Net inflow $ millions

2011

2011

2011

2011

2005–10

2011

2011

2011

Costa Rica

65.2

78.1

15.4

0.1

76

Côte d’Ivoire

74.0

159.0

..

6.2

–360

373

Croatia

57.7

99.0

36.6

0.0

10

1,378

Cuba

..

165.6

..

..

–190

..

Curaçao

..

99.9

..

..

..

42.1

103.1

25.5

..

144.6

104.0

5.2

63.3

106.2

.. 54.1

Cyprus Czech Republic Denmark Djibouti Dominica

Total external debt stock

$ millions

Total debt service % of exports of goods, services, and primary incomea

2011

2011

0

10,292

13.5

344

..

12,012

..

1,265

17

..

..

110

..

..

..

34

70

0

..

..

44

127

1,080

429

..

..

..

240

1,815

5,380

–2

..

..

3.4

..

90

1,273

13,106

–2,250

..

..

77.8

4.6

..

0

32

79

0

767

..

104.3

58.9

5.2

..

23

34

..

284

9.8

520

2,157

Dominican Republic

47.1

92.4

31.4

0.4

–140

3,650

2,295

0

15,395

10.4

Ecuador

70.7

130.4

3.4

0.3

–120

2,681

568

2

16,497

9.7

Egypt, Arab Rep.

39.0

159.4

19.8

0.2

–347

14,324

–483

–711

35,001

7.4

El Salvador

66.9

90.5

11.3

1.3

–292

3,665

247

0

11,995

21.7

Equatorial Guinea

98.5

226.4

..

0.2

20

..

737

..

..

..

Eritrea

49.8

81.9

..

6.3

55

..

19

..

1,055

..

Estonia

155.0

141.9

7.6

..

0

407

436

–112

..

..

Ethiopia

38.1

136.5

34.4

11.8

–300

513

627

..

8,597

6.1

Faeroe Islands

..

103.1

..

..

..

146

..

..

..

..

Fiji

82.8

107.8

..

2.0

–29

158

204

..

861

..

Finland

61.8

74.8

5.3

..

73

751

–5,758

–5

..

..

France

47.3

95.6

8.0

..

500

19,307

45,209

3,608

..

..

..

83.1

..

..

0

700

40

..

..

..

Gabon

95.6

218.6

..

0.5

5

..

728

..

2,879

..

Gambia, The

48.8

91.7

32.4

15.6

–14

91

36

..

466

7.5

Georgia

64.4

133.8

20.2

3.9

–150

1,537

1,154

–7

11,124

26.9

Germany

75.8

99.5

3.0

..

550

13,160

39,067

7,778

..

..

Ghana

71.4

185.3

5.4

4.8

–51

152

3,222

1

11,289

2.4

Greece

30.9

93.9

22.0

..

154

1,186

1,092

–354

..

..

..

72.4

..

..

..

..

..

..

..

..

42.4

101.9

57.0

1.6

–5

29

41

..

567

13.3

French Polynesia

Greenland Grenada Guam

..

85.0

..

..

0

..

..

..

..

..

57.7

90.9

10.5

0.9

–200

4,508

1,081

0

16,286

15.6

Guinea

73.7

109.9

0.1

4.5

–300

65

896

..

3,139

11.2

Guinea-Bissau

54.8

80.6

..

12.3

–10

46

19

..

284

..

114.5

130.0

..

6.2

–40

373

165

..

1,846

..

49.8

64.5

15.9

23.2

–240

1,551

181

0

783

0.5

Guatemala

Guyana Haiti Honduras

97.2

89.8

8.5

3.8

–100

2,811

1,043

0

4,642

16.0

152.9

94.1

5.5

..

75

2,441

9,629

–203

..

..

Iceland

72.5

90.3

9.0

..

10

21

1,107

–11

..

..

India

40.5

135.9

..

0.2

–3,000

63,818

32,190

–4,137

334,331

6.5

Indonesia

44.6

134.1

4.1

0.1

–1,293

6,924

18,160

–326

213,541

14.5

..

180.9

..

..

–186

1,330

4,150

..

19,113

1.4

119.0

211.8

1.9

1.7

–150

386

1,396

94

..

..

88.9

86.2

4.2

..

100

755

11,506

86,184

..

..

..

..

..

..

..

..

..

..

..

..

58.7

95.0

6.2

..

274

595

11,374

–821

..

..

Hungary

Iran, Islamic Rep. Iraq Ireland Isle of Man Israel

Economy

States and markets

Global links Back

World Development Indicators 2013 97


6  Global links Merchandise Net barter Inbound Net official Net Personal Foreign trade terms of tourism development migration transfers and direct trade index expenditure assistance compensation investment of employees, received

Total external debt stock

Total debt service

% of GDP

2000 = 100

% of exports

% of GNI

thousands

$ millions

Net inflow $ millions

Net inflow $ millions

$ millions

% of exports of goods, services, and primary incomea

2011

2011

2011

2011

2005–10

2011

2011

2011

2011

2011

Italy

49.2

95.6

7.2

..

1,999

7,025

28,003

5,978

..

..

Jamaica

55.4

68.6

48.1

0.4

–100

2,106

173

0

14,350

36.5

Japan

28.6

60.1

1.3

..

270

2,132

79

5,645

..

..

Jordan

91.1

76.5

29.4

3.4

203

3,453

1,469

109

17,634

6.7

Kazakhstan

67.1

219.9

1.6

0.1

7

180

13,227

39

124,437

34.6

Kenya

61.1

90.7

18.6

7.4

–189

934

335

20

10,258

4.2

Kiribati

78.0

90.1

..

27.2

..

..

4

..

..

..

..

79.6

..

..

0

..

55

..

..

..

96.7

63.1

2.7

..

–30

8,494

4,661

–7,479

..

..

..

..

..

9.9

..

1,122

546

0

1,531

8.9

Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR

69.9

219.2

..

..

278

..

400

1,099

..

..

105.1

109.4

20.3

9.7

–132

1,709

694

5

5,486

11.8

60.9

125.0

17.2

5.2

–75

110

301

11

6,158

..

103.0

110.4

6.7

..

–10

695

1,502

38

38,255

47.0

Lebanon

65.9

98.1

27.5

1.1

–13

7,322

3,476

240

24,767

19.9

Lesotho

152.5

73.7

2.1

9.0

–20

649

132

0

792

..

90.7

162.0

18.6

53.6

300

360

1,313

..

448

..

Libya

..

185.4

..

..

–20

..

200

0

..

..

Liechtenstein

..

..

..

..

..

..

..

..

..

..

139.5

102.3

4.3

..

–36

1,956

1,443

9

29,988

20.1

85.6

98.5

5.3

..

42

1,740

18,366

32,486

..

..

112.8

86.7

4.5

1.6

2

434

495

–8

6,286

18.9

Latvia

Liberia

Lithuania Luxembourg Macedonia, FYR Madagascar

45.3

74.7

..

4.2

–5

..

907

..

2,769

2.1

Malawi

62.8

98.2

2.7

14.5

–20

17

92

–1

1,202

1.3

Malaysia

144.0

100.7

7.4

0.0

84

1,198

12,001

..

94,468

3.9

Maldives

88.3

100.1

80.3

2.7

0

3

282

0

983

..

Mali

52.7

177.3

..

12.3

–101

473

178

..

2,931

..

Malta

120.2

53.6

16.1

..

5

37

467

–7

..

..

Marshall Islands

100.8

107.2

..

38.2

..

..

7

..

..

..

Mauritania

125.1

131.5

..

9.2

10

..

45

..

2,709

3.6

Mauritius

69.3

71.5

30.5

1.7

0

1

273

9,387

1,435

1.4

Mexico

61.6

107.9

3.4

0.1

–1,805

23,588

20,823

–6,244

287,037

11.2

Micronesia, Fed. Sts. Moldova Monaco Mongolia

62.8

97.3

..

41.2

–9

..

8

..

..

..

105.9

105.9

8.3

6.0

–172

1,600

294

5

5,452

12.8

..

..

..

..

..

..

..

..

..

..

129.1

226.8

4.7

4.3

–15

279

4,715

9

2,564

2.1 10.2

Montenegro

70.6

..

..

1.6

–3

343

558

–15

2,093

Morocco

65.1

140.6

25.7

1.3

–675

7,256

2,521

166

29,049

9.9

Mozambique

77.6

106.8

7.0

16.3

–20

157

2,079

0

4,097

1.6 ..

Myanmar

..

104.5

3.3

..

–500

127

1,001

..

7,765

Namibia

85.5

116.7

12.1

2.4

–1

15

969

4

..

..

Nepal

35.5

77.9

22.3

4.7

–100

4,217

94

..

3,956

9.5

150.7

..

..

..

8

..

..

..

..

..

..

230.2

..

..

6

519

1,745

0

..

..

New Zealand

46.8

132.6

11.3

..

65

875

4,285

1,594

..

..

Nicaragua

80.4

82.8

8.0

7.6

–200

914

968

0

7,121

14.8

Niger

60.7

163.9

..

10.9

–29

102

1,014

..

1,408

..

Netherlands New Caledonia

98 

Portfolio equity

World Development Indicators 2013

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Global links  6 Merchandise Net barter Inbound Net official Net Personal Foreign trade terms of tourism development migration transfers and direct trade index expenditure assistance compensation investment of employees, received

Portfolio equity

Total external debt stock

Total debt service

% of GDP

2000 = 100

% of exports

% of GNI

thousands

$ millions

Net inflow $ millions

Net inflow $ millions

$ millions

% of exports of goods, services, and primary incomea

2011

2011

2011

2011

2005–10

2011

2011

2011

2011

2011

71.3

211.4

0.7

0.8

–300

20,619

8,842

2,571

13,108

0.4

..

86.2

..

..

..

..

0

..

..

..

Norway

51.4

156.6

3.2

..

171

765

7,281

1,708

..

..

Oman

98.6

230.9

3.3

..

153

39

788

–447

..

..

Pakistan

33.2

52.4

3.6

1.6

–2,000

12,263

1,309

–37

60,182

9.2

Nigeria Northern Mariana Islands

Palau

76.1

104.2

..

20.7

..

..

2

..

..

..

133.3

86.4

12.2

0.4

11

388

3,223

0

12,583

3.6

Papua New Guinea

92.4

166.0

..

4.9

0

11

–309

..

12,582

15.8

Paraguay

74.8

107.5

2.3

0.4

–40

893

412

0

6,011

3.6

Peru

47.6

159.0

5.8

0.4

–725

2,697

8,233

147

44,872

6.5

Philippines

49.9

64.7

6.0

–0.1

–1,233

22,973

1,869

1,038

76,043

17.6

Poland

76.8

99.7

5.0

..

56

7,641

15,296

3,052

..

..

Portugal

58.6

86.8

17.3

..

150

3,778

13,074

–10,557

..

..

..

..

..

..

–145

..

..

..

..

..

Qatar

83.4

213.3

..

..

857

574

–87

–903

..

..

Romania

77.2

98.3

2.9

..

–100

3,889

2,557

–37

129,822

27.5

Russian Federation

45.5

234.2

3.0

..

1,136

4,951

52,878

–9,707

542,977

10.5

Rwanda

33.1

225.7

..

20.2

15

103

106

..

1,103

..

Samoa

62.7

77.5

67.9

16.6

–16

139

15

..

368

5.8

Panama

Puerto Rico

San Marino

..

..

..

..

..

..

..

..

..

..

São Tomé and Príncipe

57.6

120.3

54.2

30.2

–7

7

35

0

231

5.4

Saudi Arabia

82.6

215.8

2.5

..

1,056

244

16,308

..

..

..

Senegal

59.1

105.4

..

7.5

–133

1,478

286

..

4,320

..

Serbia

69.7

..

7.2

1.3

0

3,271

2,700

69

31,569

31.5

115.1

75.0

34.6

2.1

..

26

139

0

1,780

3.2

68.6

61.7

8.1

14.7

60

59

715

..

1,049

3.8

323.4

81.2

..

..

722

..

64,003

–3,754

..

..

..

..

..

..

..

11

–48

..

..

..

Slovak Republic

163.0

87.4

3.0

..

37

1,753

3,658

39

..

..

Slovenia

141.6

86.1

8.0

..

22

433

818

222

..

..

Solomon Islands

104.3

87.9

..

49.6

0

2

146

..

256

2.0

..

100.3

..

..

–300

..

102

..

3,050

..

53.5

154.1

9.1

0.3

700

1,158

5,889

–3,769

113,512

5.3 ..

Seychelles Sierra Leone Singapore Sint Maarten

Somalia South Africa South Sudan

..

..

..

..

..

..

..

..

..

Spain

44.7

99.0

14.9

..

2,250

9,907

31,419

2,978

..

..

Sri Lanka

51.0

72.1

10.4

1.0

–250

5,153

956

–623

23,984

9.3

St. Kitts and Nevis

42.1

66.8

40.8

2.5

..

48

114

..

..

..

St. Lucia

69.9

94.8

56.7

3.0

–1

29

81

..

448

7.8

St. Martin

..

..

..

..

..

..

..

..

..

..

53.8

107.2

48.3

2.8

–5

29

110

..

283

15.2

Sudan

28.3

229.4

..

2.0

135

1,420

1,936

..

21,169

..

Suriname

96.3

141.8

2.6

..

–5

4

145

0

..

..

Swaziland

97.8

105.3

..

3.2

–6

55

95

..

605

1.9

Sweden

67.1

87.2

6.2

..

266

776

3,054

2,146

..

..

Switzerland

67.1

79.8

5.0

..

183

3,307

10,077

7,543

..

..

..

143.7

..

..

–56

2,079

1,060

..

4,968

..

68.1

102.4

3.4

5.5

–296

3,060

11

0

3,323

..

St. Vincent and Grenadines

Syrian Arab Republic Tajikistan

Economy

States and markets

Global links Back

World Development Indicators 2013 99


6  Global links Merchandise Net barter Inbound Net official Net Personal Foreign trade terms of tourism development migration transfers and direct trade index expenditure assistance compensation investment of employees, received

Portfolio equity

Total external debt stock

Total debt service

$ millions

% of exports of goods, services, and primary incomea

2011

2011

% of GDP

2000 = 100

% of exports

% of GNI

thousands

$ millions

Net inflow $ millions

Net inflow $ millions

2011

2011

2011

2011

2005–10

2011

2011

2011

Tanzania

66.3

151.4

19.9

10.4

–300

76

1,095

3

10,044

2.0

Thailand

132.3

93.9

..

–0.1

492

4,554

7,780

875

80,039

3.8 ..

Timor-Leste

30.4

..

..

..

–50

131

47

..

..

Togo

77.3

30.4

..

15.5

–5

337

54

..

643

..

Tonga

44.7

81.2

..

21.1

–8

72

10

..

191

8.8

Trinidad and Tobago

103.2

152.6

..

..

–20

91

574

..

..

..

Tunisia

91.2

95.0

11.2

1.5

–20

2,005

433

–44

22,335

10.7

Turkey

48.5

88.6

15.4

0.1

–50

1,087

16,049

–986

307,007

30.2

Turkmenistan

72.7

221.2

..

0.2

–55

..

3,186

..

445

..

..

73.4

..

..

..

..

97

..

..

..

Tuvalu

70.7

..

..

76.9

..

..

2

..

..

..

Uganda

45.0

119.7

24.2

9.6

–135

949

797

106

3,858

1.7

Turks and Caicos Islands

Ukraine

91.4

124.0

6.1

0.5

–40

7,822

7,207

519

134,481

30.8

136.0

178.3

..

..

3,077

..

7,679

..

..

..

United Kingdom

45.4

101.4

5.9

..

1,020

1,796

36,244

–16,894

..

..

United States

25.0

94.6

8.8

..

4,955

5,810

257,528

27,350

..

..

Uruguay

40.1

100.9

18.7

0.0

–50

102

2,177

..

14,350

11.1

Uzbekistan

51.2

178.0

..

0.5

–518

..

1,403

..

8,382

..

Vanuatu

47.4

91.9

71.2

12.4

0

22

58

0

202

1.6

United Arab Emirates

Venezuela, RB

44.3

258.7

0.9

0.0

40

138

5,226

..

67,908

6.4

164.8

99.9

5.3

3.0

–431

8,600

7,430

1,064

57,841

3.2

Virgin Islands (U.S.)

..

..

..

..

–4

..

..

..

..

..

West Bank and Gaza

..

..

..

..

–90

1,545

..

..

..

..

Yemen, Rep.

59.0

157.5

9.2

1.5

–135

1,404

–713

0

6,418

2.8

Zambia

83.3

192.8

1.6

6.1

–85

46

1,982

11

4,360

2.1

Zimbabwe

81.8

110.5

..

7.4

–900

..

387

..

6,275

..

51.8

..

5.4b

0.2c

..

479,246

1,654,419

183,598

..

..

58.1

..

12.0 b

8.8 c

–6,808

27,628

18,331

124

133,292

4.6

51.4

..

4.7

b

0.2c

–16,352

330,766

629,613

8,474

4,742,871

8.9

53.2

..

6.5b

0.8 c

–12,699

202,300

108,774

–349

1,170,790

9.5

0.1c

–3,653

128,466

520,838

8,822

3,572,081

8.8 8.8

Vietnam

World Low income Middle income Lower middle income Upper middle income

50.9

..

4.3b

Low & middle income

51.5

..

4.8b

0.6c

–23,160

358,394

647,943

8,598

4,876,163

b

0.1c

–5,221

85,943

274,550

7,980

1,242,633

4.7

42,960

119,394

–10,115

1,484,186

17.8

East Asia & Pacific

57.2

..

3.4

Europe & Central Asia

57.0

..

5.7b

0.2c

–595

Latin America & Carib.

38.2

..

4.7b

0.2c

–5,088

59,532

161,618

7,351

1,233,484

13.4

Middle East & N. Africa

67.3

..

11.1b

..

–1,628

41,339

16,309

–145

166,124

5.1

South Asia

40.7

..

6.5b

0.7c

–8,622

97,532

35,728

–4,807

454,138

6.7

Sub-­Saharan Africa

62.7

..

6.5b

3.9c

–2,006

31,088

40,345

8,334

295,598

3.4

High income

51.9

..

5.7b

0.0 c

22,906

120,853

1,006,476

175,000

..

..

Euro area

70.8

..

6.1b

0.0 c

6,336

75,712

320,055

128,811

..

..

a. The numerator refers to 2011, whereas the denominator is a three-year average of 2009–11 data. b. Calculated using the World Bank’s weighted aggregation methodology (see Statistical methods) and thus may differ from data reported by the World Tourism Organization. c. Based on the World Bank classification of economies and thus may differ from data reported by the Organisation for Economic Co‑operation and Development.

100 

World Development Indicators 2013

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Official development assistance

changing its presentation of balance of payments data to conform

Data on official development assistance received refer to aid to

to the International Monetary Fund’s (IMF) Balance of Payments

eligible countries from members of the Organisation of Economic

Manual, 6th edition (BPM6). The historical data series based on

Co-operation and Development’s (OECD) Development Assistance

BPM5 ends with data for 2005. Balance of payments data from

Committee (DAC), multilateral organizations, and non-DAC donors.

2005 forward have been presented in accord with the BPM6 meth-

Data do not reflect aid given by recipient countries to other develop-

odology, which can be accessed at www.imf.org/external/np/sta/

ing countries or distinguish among types of aid (program, project,

bop/bop.htm.

or food aid; emergency assistance; or postconflict peacekeeping assistance), which may have different effects on the economy.

Trade in goods

Ratios of aid to gross national income (GNI), gross capital for-

Data on merchandise trade are from customs reports of goods

mation, imports, and government spending measure a country’s

moving into or out of an economy or from reports of financial

dependency on aid. Care must be taken in drawing policy conclu-

transactions related to merchandise trade recorded in the balance

sions. For foreign policy reasons some countries have traditionally

of payments. Because of differences in timing and definitions,

received large amounts of aid. Thus aid dependency ratios may

trade flow estimates from customs reports and balance of pay-

reveal as much about a donor’s interests as about a recipient’s

ments may differ. Several international agencies process trade

needs. Increases in aid dependency ratios can reflect events affect-

data, each correcting unreported or misreported data, leading to

ing both the numerator (aid) and the denominator (GNI).

other differences. The most detailed source of data on interna-

Data are based on information from donors and may not be con-

tional trade in goods is the United Nations Statistics Division’s

sistent with information recorded by recipients in the balance of

Commodity Trade Statistics (Comtrade) database. The IMF and

payments, which often excludes all or some technical assistance—

the World Trade Organization also collect customs-based data

particularly payments to expatriates made directly by the donor.

on trade in goods.

Similarly, grant commodity aid may not always be recorded in trade

The “terms of trade” index measures the relative prices of a coun-

data or in the balance of payments. DAC statistics exclude aid for

try’s exports and imports. The most common way to calculate terms

military and antiterrorism purposes. The aggregates refer to World

of trade is the net barter (or commodity) terms of trade index, or

Bank classifications of economies and therefore may differ from

the ratio of the export price index to the import price index. When a

those reported by the OECD.

country’s net barter terms of trade index increases, its exports have become more expensive or its imports cheaper.

Migration, personal transfers, and compensation of employees

Tourism

The movement of people, most often through migration, is a signifi-

Tourism is defined as the activity of people traveling to and staying

cant part of global integration. Migrants contribute to the economies

in places outside their usual environment for no more than one year

of both their host country and their country of origin. Yet reliable sta-

for leisure, business, and other purposes not related to an activity

tistics on migration are difficult to collect and are often incomplete,

remunerated from within the place visited. Data on inbound and

making international comparisons a challenge.

outbound tourists refer to the number of arrivals and departures,

Since data on emigrant stock is difficult for countries to collect,

not to the number of unique individuals. Thus a person who makes

the United Nations Population Division provides data on net migra-

several trips to a country during a given period is counted each

tion, taking into account the past migration history of a country or

time as a new arrival. Data on inbound tourism show the arrivals of

area, the migration policy of a country, and the influx of refugees

nonresident tourists (overnight visitors) at national borders. When

in recent periods to derive estimates of net migration. The data to

data on international tourists are unavailable or incomplete, the

calculate these estimates come from various sources, including

table shows the arrivals of international visitors, which include tour-

border statistics, administrative records, surveys, and censuses.

ists, same-day visitors, cruise passengers, and crew members. The

When there are insufficient data, net migration is derived through

aggregates are calculated using the World Bank’s weighted aggrega-

the difference between the growth rate of a country’s population

tion methodology (see Statistical methods) and differ from the World

over a certain period and the rate of natural increase of that popu-

Tourism Organization’s aggregates.

lation (itself being the difference between the birth rate and the

For tourism expenditure, the World Tourism Organization uses bal-

death rate).

ance of payments data from the IMF supplemented by data from

Migrants often send funds back to their home countries, which are

individual countries. These data, shown in the table, include travel

recorded as personal transfers in the balance of payments. Personal

and passenger transport items as defined by the Balance of Pay-

transfers thus include all current transfers between resident and

ments. When the IMF does not report data on passenger transport

nonresident individuals, independent of the source of income of the

items, expenditure data for travel items are shown.

sender (irrespective of whether the sender receives income from

Economy

States and markets

Global links Back links Back

World Development Indicators 2013 101


6  Global links labor, entrepreneurial or property income, social benefits, or any

by different parties during their lives. Negotiability allows investors

other types of transfers or disposes of assets) and the relationship

to diversify their portfolios and to withdraw their investment read-

between the households (irrespective of whether they are related

ily. Included in portfolio investment are investment fund shares or

or unrelated individuals).

units (that is, those issued by investment funds) that are evidenced

Compensation of employees refers to the income of border,

by securities and that are not reserve assets or direct investment.

seasonal, and other short-term workers who are employed in an

Although they are negotiable instruments, exchange-traded financial

economy where they are not resident and of residents employed by

derivatives are not included in portfolio investment because they

nonresident entities. Compensation of employees has three main

are in their own category.

components: wages and salaries in cash, wages and salaries in

External debt

kind, and employers’ social contributions.

External indebtedness affects a country’s creditworthiness and

102 

Equity flows

investor perceptions. Data on external debt are gathered through the

Equity flows comprise foreign direct investment (FDI) and portfolio

World Bank’s Debtor Reporting System (DRS). Indebtedness is cal-

equity. The internationally accepted definition of FDI (from BPM6)

culated using loan-by-loan reports submitted by countries on long-

includes the following components: equity investment, including

term public and publicly guaranteed borrowing and using information

investment associated with equity that gives rise to control or influ-

on short-term debt collected by the countries, from creditors through

ence; investment in indirectly influenced or controlled enterprises;

the reporting systems of the Bank for International Settlements, or

investment in fellow enterprises; debt (except selected debt); and

based on national data from the World Bank’s Quarterly External

reverse investment. The Framework for Direct Investment Relation-

Debt Statistics. These data are supplemented by information from

ships provides criteria for determining whether cross-border owner-

major multilateral banks and official lending agencies in major credi-

ship results in a direct investment relationship, based on control

tor countries. Currently, 128 developing countries report to the DRS.

and influence.

Debt data are reported in the currency of repayment and compiled

Direct investments may take the form of greenfield investment,

and published in U.S. dollars. End-of-period exchange rates are used

where the investor starts a new venture in a foreign country by con-

for the compilation of stock figures (amount of debt outstanding),

structing new operational facilities; joint venture, where the inves-

and projected debt service and annual average exchange rates are

tor enters into a partnership agreement with a company abroad to

used for the flows. Exchange rates are taken from the IMF’s Inter-

establish a new enterprise; or merger and acquisition, where the

national Financial Statistics. Debt repayable in multiple currencies,

investor acquires an existing enterprise abroad. The IMF suggests

goods, or services and debt with a provision for maintenance of the

that investments should account for at least 10 percent of voting

value of the currency of repayment are shown at book value.

stock to be counted as FDI. In practice many countries set a higher

While data related to public and publicly guaranteed debt are

threshold. Many countries fail to report reinvested earnings, and the

reported to the DRS on a loan-by-loan basis, data on long-term

definition of long-term loans differs among countries.

private nonguaranteed debt are reported annually in aggregate by

Portfolio equity investment is defined as cross-border transac-

the country or estimated by World Bank staff for countries. Private

tions and positions involving equity securities, other than those

nonguaranteed debt is estimated based on national data from the

included in direct investment or reserve assets. Equity securities are

World Bank’s Quarterly External Debt Statistics.

equity instruments that are negotiable and designed to be traded,

Total debt service as a share of exports of goods, services, and

usually on organized exchanges or “over the counter.” The negotia-

primary income provides a measure of a country’s ability to service

bility of securities facilitates trading, allowing securities to be held

its debt out of export earnings.

World Development Indicators 2013

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Global links  6 Definitions

Data sources

• Merchandise trade includes all trade in goods and excludes trade

Data on merchandise trade are from the World Trade Organization.

in services. • Net barter terms of trade index is the percentage ratio

Data on trade indexes are from the United Nations Conference

of the export unit value indexes to the import unit value indexes, mea-

on Trade and Development’s (UNCTAD) annual Handbook of Sta-

sured relative to the base year 2000. • Inbound tourism expenditure

tistics. Data on tourism expenditure are from the World Tourism

is expenditures by international inbound visitors, including payments

Organization’s Yearbook of Tourism Statistics and World Tourism

to national carriers for international transport and any other prepay-

Organization (2012a) and updated from its electronic files. Data

ment made for goods or services received in the destination country.

on net official development assistance are compiled by the OECD

They may include receipts from same-day visitors, except when these

(http://stats.oecd.org). Data on net migration are from United

are important enough to justify separate classification. Data include

Nations Population Division (2011). Data on personal transfers

travel and passenger transport items as defined by Balance of Pay-

and compensation of employees are from the IMF’s Balance of

ments. When passenger transport items are not reported, expendi-

Payments Statistics Yearbook supplemented by World Bank staff

ture data for travel items are shown. Exports refer to all transactions

estimates. Data on FDI are World Bank staff estimates based

between residents of a country and the rest of the world involving

on IMF balance of payments statistics and UNCTAD data (http://

a change of ownership from residents to nonresidents of general

unctadstat.unctad.org/ReportFolders/reportFolders.aspx). Data

merchandise, goods sent for processing and repairs, nonmonetary

on portfolio equity are from the IMF’s Balance of Payments Sta-

gold, and services. • Net official development assistance is flows

tistics Yearbook. Data on external debt are mainly from reports

(net of repayment of principal) that meet the DAC definition of official

to the World Bank through its DRS from member countries that

development assistance and are made to countries and territories

have received International Bank for Reconstruction and Develop-

on the DAC list of aid recipients, divided by World Bank estimates

ment loans or International Development Assistance credits, with

of GNI. • Net migration is the net total of migrants (immigrants

additional information from the files of the World Bank, the IMF,

less emigrants, including both citizens and noncitizens) during the

the African Development Bank and African Development Fund,

period. Data are five-year estimates. • Personal transfers and com-

the Asian Development Bank and Asian Development Fund, and

pensation of employees, received, are the sum of personal transfers

the Inter-American Development Bank. Summary tables of the

(current transfers in cash or in kind made or received by resident

external debt of developing countries are published annually in

households to or from nonresident households) and compensation

the World Bank’s International Debt Statistics and International

of employees (remuneration for the labor input to the production

Debt Statistics database.

process contributed by an individual in an employer-employee relationship with the enterprise). • Foreign direct investment is cross-

References

border investment associated with a resident in one economy having

IMF (International Monetary Fund). Various issues. International Finan-

control or a significant degree of influence on the management of an enterprise that is resident in another economy. • Portfolio equity is net inflows from equity securities other than those recorded as direct investment or reserve assets, including shares, stocks, depository receipts, and direct purchases of shares in local stock markets

cial Statistics. Washington, DC. ———. Various years. Balance of Payments Statistics Yearbook. Parts 1 and 2. Washington, DC. UNCTAD (United Nations Conference on Trade and Development). Various years. Handbook of Statistics. New York and Geneva.

by foreign investors • Total external debt stock is debt owed to

United Nations Population Division. 2011. World Population Prospects:

nonresident creditors and repayable in foreign currency, goods, or

The 2010 Revision. New York: United Nations, Department of Eco-

services by public and private entities in the country. It is the sum of long-term external debt, short-term debt, and use of IMF credit. • Total debt service is the sum of principal repayments and interest actually paid in foreign currency, goods, or services on long-term debt; interest paid on short-term debt; and repayments (repurchases

nomic and Social Affairs. World Bank. Various years. International Debt Statistics. Washington, DC. World Tourism Organization. 2012a. Compendium of Tourism Statistics 2012. Madrid.

and charges) to the IMF. Exports of goods and services and primary

———. 2012b Tourism Highlights: 2012 Edition. Madrid.

income are the total value of exports of goods and services, receipts

———. Various years. Yearbook of Tourism Statistics. Vols. 1 and 2.

of compensation of nonresident workers, and primary investment

Madrid.

income from abroad.

Economy

States and markets

Global links Back links Back

World Development Indicators 2013 103


6  Global links Online tables and indicators To access the World Development Indicators online tables, use

indicator online, use the URL http://data.worldbank.org/indicator/

the URL http://wdi.worldbank.org/table/ and the table number (for

and the indicator code (for example, http://data.worldbank.org/

example, http://wdi.worldbank.org/table/6.1). To view a specific

indicator/TX.QTY.MRCH.XD.WD).

6.1 Growth of merchandise trade Export volume

TX.QTY.MRCH.XD.WD

Import volume

TM.QTY.MRCH.XD.WD

Export value

TX.VAL.MRCH.XD.WD

Import value

TM.VAL.MRCH.XD.WD

Net barter terms of trade index

TT.PRI.MRCH.XD.WD

6.2 Direction and growth of merchandise trade This table provides estimates of the flow of trade in goods between groups of economies.

6.3 High-income economy trade with low- and middle‑income economies

..a

6.4 Direction of trade of developing economies Exports to developing economies within region

TX.VAL.MRCH.WR.ZS

Exports to developing economies outside region

TX.VAL.MRCH.OR.ZS

Exports to high-income economies

TX.VAL.MRCH.HI.ZS

Imports from developing economies within region

TM.VAL.MRCH.WR.ZS

Imports from developing economies outside region

TM.VAL.MRCH.OR.ZS

Imports from high-income economies

TM.VAL.MRCH.HI.ZS

LP.IMP.DURS.MD

Documents to export

IC.EXP.DOCS

Documents to import

IC.IMP.DOCS

Liner shipping connectivity index Quality of port infrastructure

This table provides historical commodity price data.

IS.SHP.GCNW.XQ IQ.WEF.PORT.XQ

Total external debt, $

DT.DOD.DECT.CD

Total external debt, % of GNI

DT.DOD.DECT.GN.ZS

Long-term debt, Public and publicly guaranteed

DT.DOD.DPPG.CD

Long-term debt, Private nonguaranteed

DT.DOD.DPNG.CD

Short-term debt, $

DT.DOD.DSTC.CD

Short-term debt, % of total debt

..a

DT.DOD.DSTC.ZS

Short-term debt, % of total reserves

DT.DOD.DSTC.IR.ZS

Total debt service

DT.TDS.DECT.EX.ZS

Present value of debt, % of GNI

DT.DOD.PVLX.GN.ZS

Present value of debt, % of exports of goods, services and primary income

DT.DOD.PVLX.EX.ZS

6.9 Global private financial flows Foreign direct investment net inflows, $

BX.KLT.DINV.CD.WD

Foreign direct investment net inflows, % of GDP

BX.KLT.DINV.WD.GD.ZS

Portfolio equity

6.5 Primary commodity prices

BX.PEF.TOTL.CD.WD

Bonds

DT.NFL.BOND.CD

Commercial banks and other lendings

DT.NFL.PCBO.CD

6.10 Net official financial flows

6.6 Tariff barriers All products, Binding coverage

TM.TAX.MRCH.BC.ZS

Simple mean bound rate

TM.TAX.MRCH.BR.ZS

Simple mean tariff

TM.TAX.MRCH.SM.AR.ZS

Weighted mean tariff

TM.TAX.MRCH.WM.AR.ZS

Share of tariff lines with international peaks

TM.TAX.MRCH.IP.ZS

Net financial flows from bilateral sources

DT.NFL.BLAT.CD

Net financial flows from multilateral sources

DT.NFL.MLAT.CD

World Bank, IDA

DT.NFL.MIDA.CD

World Bank, IBRD

DT.NFL.MIBR.CD

IMF, Concessional

DT.NFL.IMFC.CD

IMF, Non concessional

DT.NFL.IMFN.CD

Regional development banks, Concessional

DT.NFL.RDBC.CD

Manufactured products, Simple mean tariff TM.TAX.MANF.SM.AR.ZS

Regional development banks, Nonconcessional

DT.NFL.RDBN.CD

Manufactured products, Weighted mean tariff

Regional development banks, Other institutions

DT.NFL.MOTH.CD

Share of tariff lines with specific rates

TM.TAX.MRCH.SR.ZS

Primary products, Simple mean tariff

TM.TAX.TCOM.SM.AR.ZS

Primary products, Weighted mean tariff

TM.TAX.TCOM.WM.AR.ZS

TM.TAX.MANF.WM.AR.ZS

6.7 Trade facilitation

6.11 Aid dependency

Logistics performance index

LP.LPI.OVRL.XQ

Burden of customs procedures

104 

LP.EXP.DURS.MD

Lead time to import

6.8 External debt ..a

This table illustrates the importance of developing economies in the global trading system.

Lead time to export

World Development Indicators 2013

IQ.WEF.CUST.XQ

Front

?

User guide

Net official development assistance (ODA) Net ODA per capita

World view

DT.ODA.ODAT.CD DT.ODA.ODAT.PC.ZS

People

Environment


Global links  6 Grants, excluding technical cooperation

BX.GRT.EXTA.CD.WD

Technical cooperation grants

BX.GRT.TECH.CD.WD

Net ODA, % of GNI

DT.ODA.ODAT.GN.ZS

Net ODA, % of gross capital formation Net ODA, % of imports of goods and services and income

DT.ODA.ODAT.GI.ZS DT.ODA.ODAT.MP.ZS

Net migration International migrant stock Emigration rate of tertiary educated to OECD countries Refugees by country of origin Refugees by country of asylum

Net ODA, % of central government expenditure

DT.ODA.ODAT.XP.ZS

6.12 Distribution of net aid by Development Assistance Committee members Net bilateral aid flows from DAC donors

DC.DAC.TOTL.CD

United States

DC.DAC.USAL.CD

EU institutions

DC.DAC.CECL.CD

Germany

DC.DAC.DEUL.CD

France

DC.DAC.FRAL.CD

United Kingdom

DC.DAC.GBRL.CD

Japan

DC.DAC.JPNL.CD

Netherlands

DC.DAC.NLDL.CD

Australia

DC.DAC.AUSL.CD

Canada

DC.DAC.CANL.CD

Norway

DC.DAC.NORL.CD

SM.POP.NETM SM.POP.TOTL SM.EMI.TERT.ZS SM.POP.REFG.OR SM.POP.REFG

Personal transfers and compensation of employees, Received

BX.TRF.PWKR.CD.DT

Personal transfers and compensation of employees, Paid

BM.TRF.PWKR.CD.DT

6.14 Travel and tourism International inbound tourists

ST.INT.ARVL

International outbound tourists

ST.INT.DPRT

Inbound tourism expenditure, $ Inbound tourism expenditure, % of exports Outbound tourism expenditure, $ Outbound tourism expenditure, % of imports

ST.INT.RCPT.CD ST.INT.RCPT.XP.ZS ST.INT.XPND.CD ST.INT.XPND.MP.ZS

a. Available online only as part of the table, not as an individual indicator. b. Derived from data elsewhere in the World Development Indicators database.

..a,b

Other DAC donors

Economy

6.13 Movement of people

States and markets

Global links Back links Back

World Development Indicators 2013 105


106â&#x20AC;&#x192;

World Development Indicators 2013

Front

?

User guide

World view

People

Environment


Primary data documentation As a major user of socioeconomic data, the World Bank recognizes the importance of data documentation to inform users of differences in the methods and conventions used by primary data collectors—usually national statistical agencies, central banks, and customs services—and by international organizations, which compile the statistics that appear in the World Development Indicators database. These differences may give rise to significant discrepancies over time, both within countries and across them. Delays in reporting data and the use of old surveys as the base for current estimates may further compromise the quality of data reported here. This section provides information on sources, methods, and reporting standards of the principal demographic, economic, and environmental indicators in World Development Indicators. Additional documentation is available from the World Bank’s Bulletin Board on Statistical Capacity at http://data.worldbank.org. The demand for good-quality statistical data is ever increasing. Statistics provide the evidence needed to improve decisionmaking,

Economy

States and markets

document results, and heighten public accountability. The need for improved statistics to monitor the Millennium Development Goals and the parallel effort to support a culture of resultsbased management has stimulated a decadelong effort to improve statistics. The results have been impressive, but more needs to done. The “Statistics for Transparency, Accountability, and Results: A Busan Action Plan for Statistics” was endorsed by the Busan Partnership for Effective Development Cooperation at the Fourth High-level Forum for Aid Effectiveness held November 29–December 1, 2011, in Busan, Republic of Korea. This plan builds on the progress made under the first global plan to improve national and international statistics, the 2004 Marrakech Action Plan for Statistics, but goes beyond it in many ways. The main objectives of the plan are to integrate statistics into decisionmaking, promote open access to statistics within government and for all other uses, and increase resources for statistical systems, both for investment in new capacity and for maintaining current operations.

Global links

Back

World Development Indicators 2013 107


Primary data documentation Currency

National accounts

System of SNA Reference National price year Accounts valuation

Base year

Afghanistan Albania Algeria American Samoa Andorra Angola Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria

Afghan afghani 2002/03 a Albanian lek Algerian dinar 1980 U.S. dollar Euro 1990 Angolan kwanza 2002 East Caribbean dollar 2006 Argentine peso 1993 a Armenian dram Aruban florin 1995 a Australian dollar Euro 2005 a New Azeri manat Bahamian dollar 2006 Bahraini dinar 1985 Bangladeshi taka 1995/96 Barbados dollar 1974 a Belarusian rubel Euro 2005 Belize dollar 2000 CFA franc 1985 Bermuda dollar 2006 Bhutanese ngultrum 2000 Bolivian Boliviano 1990 a Bosnia and Herzegovina convertible mark Botswana pula 1993/94 Brazilian real 2000 Brunei dollar 2000 a Bulgarian lev

Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Channel Islands

CFA franc Burundi franc Cambodian riel CFA franc Canadian dollar Cape Verde escudo Cayman Islands dollar CFA franc CFA franc Pound sterling

1999 2008 2000 2000 2005 1980

Chile China Hong Kong SAR, China Macao SAR, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Curaçao

Chilean peso Chinese yuan Hong Kong dollar Macao pataca Colombian peso Comorian franc Congolese franc CFA franc Costa Rican colon CFA franc Croatian kuna Cuban peso Netherlands Antilles guilder Euro Czech koruna

2003 2000 2009 2009 2005 1990 2000 1990 1991 1996

Cyprus Czech Republic

108 

World Development Indicators 2013

2000 1995 2003

a

B B B

1996

1993 1993 1968 1968 1968 1993 1968 1993 1993 1993 2008 1993 1993 1993 1968 1993 1968 1993 1993 1993 1968 1993 1993 1968 1993

2002

1993 1993 1993 1993

B B P B B B B B B P

2007

1993 1993 1993 1993 1993 1968 1993 1968 1993 1968 1993 1993 1993 1993 1993 1968 1968 1968 1993 1968 1993 1993 1993

B P B B B P B P B P B B

1993 1993

B B

1996

1996 2009 2003

2000

2005

2005

a

2000

2005

Front

?

P B B B B B B B P B B B B B P B B B B

Balance of payments and trade

Balance of Payments Manual in use

External debt

System of trade

Accounting concept

Rolling

6 6

A A A

C C B

G G G

1991–96

2005

A

1971–84 1990–95

2005 2005

6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

G G S S S S G S S S G S G G G G G G S G S G G G S

C C

G G S S

C C B B B C B C C B B

S S G G G G G S S G G

C C C

G G

2005 2005 2005 1978–89, Rolling 1991–92 1992–93 2005 2005 2005 2005 2008 2005

6 6

A A

B C

6

A

G G S S

C

G S G S

6 6 6 6 6 6

A A A A

B C C B C C

G G G G S G

2005 2005

6 6

P E

G S S S G G G S S

B

G G

2008 2005 2005 2005 2005 2005 2005 2005

6 6 6 6 6

A P

S S G G G S S S S S G S

C B C C B

S G S G S

C C C C C

G G S G S

G S

C C

S S

Alternative conversion factor

PPP survey year

2008 Rolling 1992–95 2005 2005 2005 1990–95

2005 Rolling

1992

2005

1960–85

2005 2005 Rolling

B B B

User guide

Government IMF data finance dissem­ ination standard

1978–93

1992–94 1999–2001 1993

2005 Rolling

6 6 6 6 6

Rolling Rolling

6 6

World view

A A

A

A A A A A A A

A

A A A P A A

People

Environment


Latest population census

Latest demographic, education, or health household survey

Source of most recent income and expenditure data

Afghanistan Albania Algeria American Samoa Andorra Angola Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina

1979 2011 2008 2010 2011b 1970 2011 2010 2011 2010 2011 2011b 2009 2010 2010 2011 2010 2009 2011 2010 2002 2010 2005 2012 1991

MICS, 2010/11 DHS, 2009 MICS, 2012

MICS, 2010 DHS, 2008 MICS, 2011/12

IHS, 2010 IHS, 2009 LSMS, 2007

Botswana Brazil Brunei Darussalam Bulgaria

2011 2010 2011 2011

DHS, 1988 LSMS, 1996/97

ES/BS, 2008 LFS, 2009

LSMS, 2007

ES/BS, 2007

Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Channel Islands

2006 2008 2008 2005 2011 2010 2010 2003 2009 2009, 2011c 2012 2010 2011d 2011 2006 2003 1984 2007 2011 1998 2011 2012

DHS, 2011 DHS, 2010 DHS, 2010 DHS, 2011 DHS, 2005

CWIQ, 2009 CWIQ, 2006 IHS, 2008 PS, 2007 LFS, 2000 ES/BS, 2007

MICS, 2010 MICS, 2010

PS, 2008 PS, 2002/03

NSS, 2007

IHS, 2009 IHS, 2008

Chile China Hong Kong SAR, China Macao SAR, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Curaçao Cyprus Czech Republic

Economy

2011 2011

Vital Latest Latest registration agricultural industrial complete census data

IHS, 2008 LSMS, 2008 IHS, 1995

2013/14 Yes Yes Yes

MIS, 2011; IBEP, 2008/09

IHS, 2000

MICS, 2011 DHS, 2010

IHS, 2011 IHS, 2010

DHS, 2006

ES/BS, 1994 IS, 2000 ES/BS, 2008

Yes Yes Yes Yes Yes Yes Yes

2009

MICS, 2011 DHS, 2011/12

IHS, 2010

1964/65 2007 2013 2013/14 2011 2010

2002

2006 2009 2009

2008 Yes Yes Yes

ES/BS, 2009 IHS, 2000 ES/BS, 2011 CWIQ, 2007

2010

2009 2009

2011/12 Yes 2009 2013

2001

2006

2009 2007

2010

2009

Yes

Yes Yes

2010

Yes Yes Yes

2013 1984 2011

2007 2007

Yes Yes DHS, 2010 DHS, 2012 MICS, 2010 DHS, 2011/12 MICS, 2011 DHS, 2012

IHS, 2011 IHS, 2004 1-2-3, 2004/05 CWIQ/PS, 2011 LFS, 2011 IHS, 2008 ES/BS, 2008

2013

Yes Yes Yes

MICS, 2010/11

RHS, 1993

States and markets

Yes Yes

IS, 1996

Global links

2000 2002 2008

2011

Yes

Back

Latest water withdrawal data

2011 2011 2011

2000 2006 2001

2007

Yes DHS, 2011 MICS, 2012 MICS, 2012

Latest trade data

1990 1985/86 1973 2001 2010

2010 2010

2008 2007 2009 2009 2005

2009 2009

2009 2007

2006 1991 2011 2011 2011 2011 2011 2011 2011 2011 2011 2007 2011 2011 2011 2011 2010 2011 2011 2011 2011

2005 2005 2000 2007 2000 2002 2005 2003 2008 2005 2000 2007 2000 2001 2008 2000 2009

2011 2011 2006 2011

2000 2006 1994 2009

2011 2010 2011 2011 2011 2011

2001 2000 2006 2000 1986 2001

2011 1995

2005 2005

2011 2011 2011 2007 2011 2007 1987 2010 2011 2011 2011 2006

2007 2005

2000 1999 2005 2002 1997 2005 2010 2007

2011 2011

2009 2007

World Development Indicators 2013 109


Primary data documentation Currency

National accounts

System of SNA Reference National price year Accounts valuation

Base year

Denmark Djibouti Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Faeroe Islands Fiji Finland France French Polynesia Gabon Gambia, The Georgia Germany Ghana Greece Greenland Grenada Guam Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Iran, Islamic Rep. Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya

Danish krone 2005 Djibouti franc 1990 East Caribbean dollar 2006 Dominican peso 1991 U.S. dollar 2000 Egyptian pound 1991/92 U.S. dollar 1990 CFA franc 2000 Eritrean nakfa 2000 Euro 2005 Ethiopian birr 1999/2000 Danish krone Fijian dollar 2005 Euro 2005 a Euro CFP franc 1990/91 CFA franc 1991 Gambian dalasi 2004 a Georgian lari Euro 2005 New Ghanaian cedi 2006 a Euro Danish krone 1990 East Caribbean dollar 2006 U.S. dollar Guatemalan quetzal 2001 Guinean franc 2003 CFA franc 2005 Guyana dollar 2006 Haitian gourde 1986/87 Honduran lempira 2000 a Hungarian forint Iceland krona 2005 Indian rupee 2004/05 Indonesian rupiah 2000 Iranian rial 1997/98 Iraqi dinar 1997 Euro 2005 Pound sterling 2003 Israeli new shekel 2005 Euro 2005 Jamaican dollar 2007 Japanese yen 2005 Jordanian dinar 1994 a Kazakh tenge Kenyan shilling 2001

Kiribati Korea, Dem. Rep.

Australian dollar Democratic People’s Republic of Korean won Korean won Euro Kuwaiti dinar Kyrgyz som Lao kip Latvian lats Lebanese pound Lesotho loti

Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho

110 

World Development Indicators 2013

1993 1968 1993 1993 1993 1993 1968 1968 1968 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1968 1993 1993 1993 1993 1993 1968 1993 1993 1993 1993 1993 1993 1968 1993 1968 1993 1993 1993 1993 1968 1993 1993

B B B B B B B B B B B B B B B

2006

1993 1968

B

2005 2008 1995

1993 1993 1968 1993 1993 1993 1993 1993

B

a

2005

1996

2005

2005

2000

1995

2002 2000 1997 1995

Front

?

P P B B B B

Balance of payments and trade

Alternative conversion factor

PPP survey year

Balance of Payments Manual in use

External debt

System of trade

Accounting concept

6 6 6 6 6 6 6

S G S G G G S G

C

A A A A A A

C B C C

S G G G S S S

S G G G G S S S G G S G S G S G S S G S G S S G G S S

C B

S G

B C C

G S S

C C C B C

G G S S G S

B

G

B B

G

C

G G G G G G S S S S G G S

S S G G G G G

C C C C B C B

S S G S S S G

Rolling 2005

2005 2005 1965–84

2005 E

2005 Rolling Rolling

6 6 6 6 6 6 6

2005 2005 1990–95 2005 Rolling 1973–87 2005 Rolling

6 6 6 6 6 6

A A A

6

A

6 6 6 6 6 6 6 6 6 6 6 6 6

A P E A A A

1987–95 Rolling 2005

1993

B B B B B B B B B B P B B B P B B B B B B

2005 2005 1991 1988–89 Rolling Rolling 2005 2005 1980–2002 2005 1997, 2004 2005 Rolling 2008 Rolling

1987–95

Government IMF data finance dissem­ ination standard

2008 2005 2005 2005

6 6 6 6 6 6 6

A A

A

A A A

A A A A

6 6 2008

C C C C B C

G

6

G

G

C

S S S S G G

B B B C B C

A P B B B B B

User guide

2005 2005 2005 1987–95 Rolling 2005 2005

1990–95

World view

6 6 6 6 6 6

A P A A A

People

S G G S S G G

Environment


Latest population census

Denmark Djibouti Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Faeroe Islands Fiji Finland France French Polynesia Gabon Gambia, The Georgia Germany Ghana Greece Greenland Grenada Guam Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Iran, Islamic Rep. Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya

2011 2009 2011 2010 2010 2006 2007 2002 1984 2012 2007 2011 2007 2010 2006e 2007 2003 2003 2002 2011e 2010 2011 2010 2011 2010 2012 1996 2009 2012 2003 2001 2011 2011 2011 2010 2011 1997 2011 2011 2009 2012 2011 2010 2004 2009 2009

Kiribati Korea, Dem. Rep.

2010 2008

Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho

2010 2011 2010 2009 2005 2011 1970 2006

Economy

Latest demographic, education, or health household survey

Source of most recent income and expenditure data

MICS, 2006

ITR, 1997 PS, 2002

DHS, 2007 RHS, 2004 DHS, 2008 RHS, 2008

IHS, 2011 LFS, 2011 ES/BS, 2008 IHS, 2010

Vital Latest Latest registration agricultural industrial complete census data

Yes

2010

2008

Yes Yes

2012/13 2013/15 2010 2008

2008 2006

Yes

2010

2009 2009 2009

Yes Yes Yes Yes Yes

2009 2010 2010

2008 2009 2009

2001/02

2004 2009 2009 2003 2007

Yes

DHS, 2002 DHS, 2011

ES/BS, 2004 ES/BS, 2010/11 ES/BS, 2009 IS, 2000 ES/BS, 1994/95

DHS, 2012 MICS, 2010 MICS, 2005 MICS, 2011

CWIQ/IHS, 2005 IHS, 2010 IHS, 2009 IHS, 2000 LSMS, 2005/06 IHS, 2000

Yes Yes Yes Yes Yes Yes Yes

RHS, 1985 RHS, 2008/09 DHS, 2005 MICS, 2010 DHS, 2009 DHS, 2012 DHS, 2011/12

DHS, 2006 DHS, 2012 DHS, 2000 MICS, 2011

MICS, 2011 DHS, 2012 MICS, 2010/11 MIS, 2010; HIV/MCH SPA, 2010

LSMS, 2011 CWIQ, 2012 CWIQ, 2010 IHS, 1998 IHS, 2001 IHS, 2010 ES/BS, 2007

Yes Yes

IHS, 2010 IHS, 2012 ES/BS, 2005 IHS, 2007 IHS, 2000

Yes Yes Yes Yes

ES/BS, 2001 ES/BS, 2000 LSMS, 2010 IS, 1993 ES/BS, 2010 ES/BS, 2011 IHS, 2005/06

Yes

2010 2013/14 2009 2012 2013

2008 2013 2010 2010 2011 2013 2003 2012 2010

2010 2007 2010 2007

2009 2005 2008 2008 2008 2009 2008 2009 2007 2009

Yes 2007

Latest trade data

Latest water withdrawal data

2011 2009 2010 2011 2011 2011 2011

2009 2000 2004 2005 2005 2000 2007 2000 2004 2007 2002

2003 2011 2011 2009 2010 2011 2011 2011 2009 2011 2010 2011 2011 2011 2011 2009

2000 2005 2007 2005 2000 2005 2007 2000 2007 2005

2011 2008 2005 2011 1997 2009 2011 2011 2011 2011 2011 2009 2011

2006 2001 2000 2000 2000 2006 2007 2005 2010 2000 2004 2000 1979

2011 2011 2010 2011 2011 2011 2010

2004 2000 1993 2001 2005 2010 2003

2011 MICS, 2009

2005 ES/BS, 1998 IHS, 2009

FHS, 1996 DHS, 2012 MICS, 2011/12 MICS, 2000 DHS, 2009

States and markets

Yes Yes Yes

ES/BS, 2010 ES/BS, 2008 IHS, 2008

Yes Yes

ES/BS, 2002/03

Global links

Back

2000

2002 2010/11 2010 2011 2010

2008

2011

2002

2009 2009

2009 2011 1974 2011 2011 2009

2002 2006 2005 2002 2005 2000

2009 2007

World Development Indicators 2013â&#x20AC;&#x192;111


Primary data documentation Currency

National accounts

System of SNA Reference National price year Accounts valuation

Base year

Liberia Libya Liechtenstein Lithuania Luxembourg Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mexico Micronesia, Fed. Sts. Moldova Monaco Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nepal Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Northern Mariana Islands Norway Oman Pakistan Palau Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Romania Russian Federation Rwanda Samoa San Marino São Tomé and Príncipe Saudi Arabia Senegal Serbia

112 

Liberian dollar 2000 Libyan dinar 1999 Swiss franc 1990 Lithuanian litas 2000 a Euro Macedonian denar 1995 Malagasy ariary 1984 Malawi kwacha 2007 Malaysian ringgit 2000 Maldivian rufiyaa 2003 CFA franc 1987 Euro 2005 U.S. dollar 2004 Mauritanian ouguiya 2004 Mauritian rupee 2006 Mexican peso 2003 U.S. dollar 2004 a Moldovan leu Euro 1990 Mongolian tugrik 2005 Euro 2000 Moroccan dirham 1998 New Mozambican metical 2003 Myanmar kyat 2005/06 Namibian dollar 2004/05 Nepalese rupee 2000/01 a Euro CFP franc 1990 New Zealand dollar 2005/06 Nicaraguan gold cordoba 2006 CFA franc 1987 Nigerian naira 2002 U.S. dollar a Norwegian krone Rial Omani 1988 Pakistani rupee 1999/ 2000 U.S. dollar 1995 Panamanian balboa 1996 Papua New Guinea kina 1998 Paraguayan guarani 1994 Peruvian new sol 1994 Philippine peso 2000 a Polish zloty Euro 2005 U.S. dollar 1954 Qatari riyal 2001 a New Romanian leu Russian ruble Rwandan franc Samoan tala Euro São Tomé and Príncipe dobra Saudi Arabian riyal CFA franc New Serbian dinar

World Development Indicators 2013

2000 2006 2002 1995 2001 1999 1999 a

2005

1996

2005

2005

2005

2005

2000

1987 2002

Front

1968 1993 1993 1993 1993 1993 1968 1993 1993 1993 1968 1993 1968 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1968 1993 1993 1993 1993 1993 1993 1993 1993 1968 1993 1993 1993

P B B B B B B B P B B B B B B B B B

1993 1993 1993 1993 1993 1993 1993 1993 1968 1993 1993

B B B P B P B B P P B

1993 1993 1993 1993 1993

B P B B P

1993 1993 1993

P B B

?

B B B B P B B B B B P B

Balance of payments and trade

Alternative conversion factor

Balance of Payments Manual in use

External debt

System of trade

Accounting concept

S G S G S S S G G G S G G S G G

B

G G

C C

B C B C

S S S G G S G G S

C C

G S S

C

S

C

G G S G

A A A

G S G S S S G G G S S G G S G

A

G G G

2005

6 6

A

1990–95 Rolling Rolling Rolling 2005 2005 2005 2005 2005 Rolling

6 6 6 6 6 6 6 6 6

A

2005 2005 2008

6 6 6

A A A

2005

6

A

2005 Rolling 2005 1992–95 2005

6 6 6 6 6 6 6 6

A A A A E

1986

1990–95

2005 2005 Rolling 2008 1965–95 1993 1971–98

B P B

User guide

PPP survey year

1985–90

A A A E A A

A

2005 2005

6 6 6 6

Rolling 2005 2005

6 6 6

2005 2005 2005 Rolling Rolling

6 6 6 6 6 6 6

A A A A A

6

A

S S G S S G S S G S S

6 6 6

P A A

G G S

2005

6

A

S

2005 2005 Rolling

6 6 6

A A

S G S

1989

2005 1987–89, Rolling 1992 1987–95 2008 1994 2005

Government IMF data finance dissem­ ination standard

C

C C B C C

G G S

C B B B

G G G

C B B

S G G

C B B C B C C

G G G S S S S

B C

G S

C C

S G G G G

C

World view

People

B C

G G G

Environment


Latest population census

Latest demographic, education, or health household survey

Vital Latest Latest registration agricultural industrial complete census data

Liberia Libya Liechtenstein Lithuania Luxembourg Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mexico Micronesia, Fed. Sts. Moldova Monaco Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nepal Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Northern Mariana Islands Norway Oman Pakistan

2008 2006 2010 2011 2011 2011 1993 2008 2010 2006 2009 2011 2011 2000 2011 2010 2010 2004 2008 2010 2011 2004 2007 1983 2011 2011 2011 2009 2006 2005 2012 2006 2010 2011 2010 1998

Palau Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Romania

2010 2010 2011 2012 2007 2010 2011 2011 2010 2010 2011

Russian Federation Rwanda Samoa San Marino São Tomé and Príncipe

2010 2012 2011 2010 2012

LSMS, 1992 Special DHS, 2011 DHS, 2009

IHS, 2010 IHS, 2011

DHS, 2008/09

PS, 2009/10

2011/12

Saudi Arabia Senegal Serbia

2010 2002 2011

Demographic survey, 2007 Continuous DHS, 2012 MICS, 2010

PS, 2010/11 IHS, 2010

2010 2011/12 2012

Economy

MIS, 2011

Source of most recent income and expenditure data

CWIQ, 2007 Yes Yes Yes Yes

ES/BS, 2008 MICS, 2011 MIS, 2011 MIS, 2012; LSMS, 2010/11 DHS, 2009 DHS, 2012

ES/BS, 2009 PS, 2010 IHS, 2010/11 ES/BS, 2009 IHS, 2010 IHS, 2009/10

Yes Yes Yes

MICS, 2011 RHS, 1991 ENPF, 1995 MICS, 2012 MICS, 2010 MICS, 2005/06 MICS/PAPFAM, 2006 DHS, 2011 MICS, 2009/10 HIV/MCH, 2009 DHS, 2011

RHS, 2006/07 DHS, 2012 MICS, 2011

Yes Yes Yes Yes

LSMS, 2011 ES/BS, 2011 ES/BS, 2007 ES/BS, 2008/09 ES/BS, 2009 LSMS, 2011 IHS, 1999

Yes Yes Yes

IS, 1997 LSMS, 2009 CWIQ/PS, 2008 IHS, 2010

Yes

IHS, 2008

1984 2010

2000 2000 2007 1999 2007 2000 2005 2005 2008 2000 2002

2007 2012

2010

2008

2011 2011 2011 2011 2011 2011 2011 2010 2011

2007

2007 2007

2011 2011 2011

2005 2003 2009

2011

2009

2011

2012 2010 2012 2010 2011/12

2008

2012 2010

2008 2008

2012 2011 2008 2007

2008

2007 2011 2010 2011 2010 2011 2011 2011 2011 2011 2011 2011 2011

2007 2009 2009 2010 2000 2001 2000 2002 2006 2008

2010 2010

2008 2007 2006

2011 2011 2011

2006 2003 2008

2011

2001

2008 2012 2012 2010 2009 2007

2002 2007 2006 2009 2009 2006 2006 2009

2011 2004 2011 2011 2011 2011 2011

2000 2005 2000 2000 2009 2009 2002 2005 2005 2009

Yes LFS, 2010 IHS, 2000 ES/BS, 2010

Latest water withdrawal data

2009 2009 2009 2006 2009 2008

IHS, 2008

IS, 2000 MICS, 2012 DHS, 2012

2010 2010 2007

Latest trade data

2009 2003

2002

2002 2001 2005 2005

Yes LSMS, 2008 LSMS, 1996 RHS, 2008 Continuous DHS, 2012 DHS, 2008

RHS, 1995/96 MICS, 2012 RHS, 1999

LFS, 2010 IHS, 1996 IHS, 2011 IHS, 2011 ES/BS, 2009 ES/BS, 2010 IS, 1997

Yes Yes Yes Yes Yes Yes

LFS, 2010

Yes

2010 2006 2008 2009

2009

2010 2011 2011 2011 2011

2001 2000

2010

1993

2011 2011 2011

2006 2002 2009

Yes

States and markets

Global links

Yes

Back

2006 2009 2009

World Development Indicators 2013 113


Primary data documentation Currency

National accounts

System of SNA Reference National price year Accounts valuation

Base year

Seychelles Sierra Leone Singapore Sint Maarten

Seychelles rupee 2006 Sierra Leonean leone 2006 Singapore dollar 2005 Netherlands Antilles guilder Slovak Republic Euro 2005 a Slovenia Euro Solomon Islands Solomon Islands dollar 2004 Somalia Somali shilling 1985 South Africa South African rand 2005 South Sudan South Sudanese pound Spain Euro 2005 Sri Lanka Sri Lankan rupee 2002 St. Kitts and Nevis East Caribbean dollar 2006 St. Lucia East Caribbean dollar 2006 St. Martin Euro St. Vincent and Grenadines East Caribbean dollar 2006 Sudan Sudanese pound 1981/82f Suriname Suriname dollar 1990 Swaziland Swaziland lilangeni 2000 a Sweden Swedish krona Switzerland Swiss franc 2005 Syrian Arab Republic Syrian pound 2000 a Tajikistan Tajik somoni a Tanzania Tanzanian shilling Thailand Timor-Leste Togo Tonga Trinidad and Tobago

1993 1993 1993 1993

P B B

1993 1993 1993 1968 1993 1993 1993 1993 1993 1968 1993 1993 1968 1993 1993 1993 1993 1968 1993 1993

B B B B B

1988 2000 2000 2010/11 2000

1993 2008 1968 1993 1993

1990 1998

1993 1993 1993

Tunisia Turkey Turkmenistan

Thai baht U.S. dollar CFA franc Tongan pa’anga Trinidad and Tobago dollar Tunisian dinar New Turkish lira New Turkmen manat

Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela, R.B. Vietnam Virgin Islands (U.S.) West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

U.S. dollar Australian dollar 2005 Ugandan shilling 2001/02 a Ukrainian hryvnia U.A.E. dirham 2007 Pound sterling 2005 a U.S. dollar Uruguayan peso 2005 a Uzbek sum Vanuatu vatu 2006 Venezuelan bolivar fuerte 1997 Vietnamese dong 1994 U.S. dollar 1982 Israeli new shekel 1997 Yemeni rial 1990 Zambian kwacha 1994 U.S. dollar 2009

114 

World Development Indicators 2013

a

2005

1996

2005

2000 2001

2007

2003

2005 1997

Front

1993 1968 1968 1993 1993 1993 1993 1993 1993 1993 1993 1993 1968 1968 1993 1968 1993

?

Balance of payments and trade

Alternative conversion factor

Balance of Payments Manual in use

2005 2005

6 6 6

Rolling Rolling

6 6 6

1977–90

B P B B B B B B B B B B B

PPP survey year

2005

6

Rolling 2005

6 6 6 6

External debt

System of trade

Accounting concept

A A

G S G

C B C

G G S

S S S

C C

S S G

G

C

S

S G S S

C B C

S G G G

S G G G G S S G G

B B

G G G G S S G G G

S G S G S

C

C

S G G G G

G S G

C B

S S

B C B C C C

G S G S S S

C C

G G G

B B B C

S G G G

A E P

A A A A

2005 Rolling Rolling 1970–2010 2005 1990–95 2005 2005

6 6 6 6 6 6 6 6 6

P P P B B

2005

6

A

2005

6 6 6

A A

B B B

2005 Rolling

6 6 6

A A E

P B B P B B B B P B P B P B B

User guide

2005

1987–95, 1997–2007

1991

2005 2005

6 6 6 6 6 6 6 6 6 6

1990–96 1990–92 1991, 1998

2005 2005 2005

6 6 6 6

1987–95

2005 2005 Rolling 2008 2005

1990–95

World view

Government IMF data finance dissem­ ination standard

E

E A A

G G G G G G G G G G G G G S S S G

A A

A A E A A

A P A

People

B C C C C

B

Environment


Latest population census

Latest demographic, education, or health household survey

Seychelles Sierra Leone Singapore Sint Maarten

2010 2004 2010 2001

MICS, 2010 General household, 2005 Population Census, 2011

Slovak Republic Slovenia Solomon Islands Somalia South Africa South Sudan Spain Sri Lanka St. Kitts and Nevis St. Lucia St. Martin St. Vincent and Grenadines Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania

2011 2011b 2009 1987 2011 2008 2011 2012 2011 2010 2011 2008 2012 2007 2011 2010 2004 2010 2012

Thailand Timor-Leste Togo Tonga Trinidad and Tobago

2010 2010 2010 2006 2011

Tunisia Turkey Turkmenistan

2004 2011 2012

Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela, R.B. Vietnam Virgin Islands (U.S.) West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

2012 2012 2002 2001 2010 2001 2010 2011 1989 2009 2011 2009 2010 2007 2004 2010 2012

Source of most recent income and expenditure data

Vital Latest Latest registration agricultural industrial complete census data

IHS, 2007 IHS, 2011

Yes Yes

IS, 2009 ES/BS, 2004 IHS, 2005/06 MICS, 2006 DHS, 2003

2011

DHS, 2006/07

ES/BS, 2010 ES/BS, 2009 IHS, 2000 ES/BS, 2010

MICS, 2012

IHS, 1995

SHHS, 2010 MICS, 2010 MICS, 2010

ES/BS, 2009 ES/BS, 1999 ES/BS, 2009/10 IS, 2000 ES/BS, 2000 ES/BS, 2004 LSMS, 2009 ES/BS, 2011

Yes Yes

Yes Yes Yes Yes

2009

2001 2010 2012/13

2009 2009

2012

2009

2010 2013

2009 2008

Yes

MICS, 2006 LSMS, 2009 AIS, 2011/12; LSMS, 2010/11 MICS, 2012 DHS, 2009/10 MICS, 2010

Yes

2008

Yes Yes

2010 2008 1981 2013 2007/08

2009 2007 2009

2013 2012 2011/12

2006

IHS, 2010 LSMS, 2007 CWIQ, 2011

MICS, 2011

IHS, 1992

MICS, 2011 DHS, 2003 MICS, 2011

IHS, 2010 LFS, 2009 LSMS, 1998

2001 2004

Yes

2007

2006 2004

2006 2008

Yes Yes

DHS, 2011 MICS, 2012

MICS, 2012 MICS, 2006 MICS, 2007 MICS, 2000 MICS, 2010/11 MICS, 2010 MICS, 2006 DHS, 2007 DHS, 2010/11

PS, 2009/10 ES/BS, 2009

Yes

IS, 1999 LFS, 2000 IHS, 2011 ES/BS, 2003

Yes Yes Yes Yes

IHS, 2011 IHS, 2010

Yes Yes Yes

IHS, 2009 ES/BS, 2005 IHS, 2010 IHS, 2003/04

2008 2012/13 2012 2010 2007 2011 2007 2007 2011 2007 1971

Latest trade data

Latest water withdrawal data

2008 2002 2011

2005 2005 1975

2011 2011 2011 1983 2011

2007 2009 2003 2000

2011 2011 2011 2008

2008 2005

2011 2009 2011 2007 2011 2011 2010 2000 2011

1995 2005g 2000 2000 2007 2000 2005 2006 2002

2011 2005 2011 2011 2010

2007 2004 2002 2000

2011 2011 2000

2001 2003 2004

2005

2009 2008 2008

2011 2008 2011 2011 2008 2011 2011 2009

2000

2011 2011 2010

2000 2005

2011 2011 2011 2011

2005 2005 2002 2002

2000 2004

2009 2006

2002 2005 2005 2007 2005 2000 2005

Note: For explanation of the abbreviations used in the table, see notes following the table. a. Original chained constant price data are rescaled. b. Population data compiled from administrative registers. c. Latest population census: Guernsey, 2009; Jersey, 2011. d. The population censuses for 1986 and 1996 were based on a one-in-seven sample of the population, while that for 2006 was based on a one-in-ten sample of the population. e. Rolling census based on continuous sample survey. f. Reporting period switch from fiscal year to calendar year from 1996. Pre-1996 data converted to calendar year. g. Includes South Sudan.

Economy

States and markets

Global links

Back

World Development Indicators 2013 115


Primary data documentation notes

116 

• Base year is the base or pricing period used for con-

on withdrawal from customs storage. Exports under

demographic, education, or health household survey

stant price calculations in the country’s national

the general system comprise outward-moving goods:

indicates the household surveys used to compile the

accounts. Price indexes derived from national accounts

(a) national goods wholly or partly produced in the coun-

demographic, education, and health data in section 2.

aggregates, such as the implicit deflator for gross

try; (b) foreign goods, neither transformed nor declared

AIS is HIV/AIDS Indicator Survey, DHS is Demographic

domestic product (GDP), express the price level rela-

for domestic consumption in the country, that move

and Health Survey, ENPF is National Family Planning

tive to base year prices. • Reference year is the year

outward from customs storage; and (c) nationalized

Survey, FHS is Family Health Survey, HIV/MCH is

in which the local currency constant price series of a

goods that have been declared for domestic consump-

HIV/Maternal and Child Health, IBEP is Integrated Sur-

country is valued. The reference year is usually the

tion and move outward without being transformed.

vey on Population Welfare, LSMS is Living Standards

same as the base year used to report the constant

Under the special system of trade, exports are catego-

Measurement Study Survey, MICS is Multiple Indicator

price series. However, when the constant price data

ries a and c. In some compilations categories b and c

Cluster Survey, MIS is Malaria Indicator Survey, NSS

are chain linked, the base year is changed annually, so

are classified as re-exports. Direct transit trade—

is National Sample Survey on Population Change,

the data are rescaled to a specific reference year to

goods entering or leaving for transport only—is

PAPFAM is Pan Arab Project for Family Health, RHS is

provide a consistent time series. When the country has

excluded from both import and export statistics. • Gov-

Reproductive Health Survey, SHHS is Sudan House-

not rescaled following a change in base year, World

ernment finance accounting concept is the account-

hold Health Survey, and SPA is Service Provision

Bank staff rescale the data to maintain a longer histori-

ing basis for reporting central government financial

Assessments. Detailed information for AIS, DHS, MIS,

cal series. To allow for cross-country comparison and

data. For most countries government finance data have

and SPA are available at www.measuredhs.com; for

data aggregation, constant price data reported in

been consolidated (C) into one set of accounts captur-

MICS at www.childinfo.org; and for RHS at www.cdc

World Development Indicators are rescaled to a com-

ing all central government fiscal activities. Budgetary

.gov/reproductivehealth. • Source of most recent

mon reference year (2000) and currency (U.S. dollars).

central government accounts (B) exclude some central

income and expenditure data shows household sur-

• System of National Accounts identifies whether a

government units. • IMF data dissemination standard

veys that collect income and expenditure data. Names

country uses the 1968, 1993, or 2008 System of

shows the countries that subscribe to the IMF’s Spe-

and detailed information on household surveys can be

National Accounts (SNA). • SNA price valuation shows

cial Data Dissemination Standard (SDDS) or General

found on the website of the International Household

whether value added in the national accounts is

Data Dissemination System (GDDS). S refers to coun-

Survey Network (www.surveynetwork.org). Core Wel-

reported at basic prices (B) or producer prices (P). Pro-

tries that subscribe to the SDDS and have posted data

fare Indicator Questionnaire Surveys (CWIQ), devel-

ducer prices include taxes paid by producers and thus

on the Dissemination Standards Bulletin Board at

oped by the World Bank, measure changes in key social

tend to overstate the actual value added in production.

http://dsbb.imf.org. G refers to countries that sub-

indicators for different population groups—specifically

However, value added can be higher at basic prices

scribe to the GDDS. The SDDS was established for

indicators of access, utilization, and satisfaction with

than at producer prices in countries with high agricul-

member countries that have or might seek access to

core social and economic services. Expenditure

tural subsidies. • Alternative conversion factor identi-

international capital markets to guide them in providing

survey/­budget surveys (ES/BS) collect detailed infor-

fies the countries and years for which a World Bank–

their economic and financial data to the public. The

mation on household consumption as well as on gen-

estimated conversion factor has been used in place of

GDDS helps countries disseminate comprehensive,

eral demographic, social, and economic characteris-

the official exchange rate (line rf in the International

timely, accessible, and reliable economic, financial,

tics. Integrated household surveys (IHS) collect

Monetary Fund’s [IMF] International Financial Statis-

and sociodemographic statistics. IMF member coun-

detailed information on a wide variety of topics, includ-

tics). See Statistical methods for further discussion of

tries elect to participate in either the SDDS or the

ing health, education, economic activities, housing,

alternative conversion factors. • Purchasing power

GDDS. Both standards enhance the availability of

and utilities. Income surveys (IS) collect information

parity (PPP) survey year is the latest available survey

timely and comprehensive data and therefore contrib-

on the income and wealth of households as well as

year for the International Comparison Program’s esti-

ute to the pursuit of sound macroeconomic policies.

various social and economic characteristics. Income

mates of PPPs. • Balance of Payments Manual in use

The SDDS is also expected to improve the functioning

tax registers (ITR) provide information on a population’s

refers to the classification system used to compile and

of financial markets. • Latest population census

income and allowance, such as gross income, taxable

report data on balance of payments. 6 refers to the 6th

shows the most recent year in which a census was

income, and taxes by socioeconomic group. Labor

edition of the IMF’s Balance of Payments Manual

conducted and in which at least preliminary results

force surveys (LFS) collect information on employment,

(2009). • External debt shows debt reporting status

have been released. The preliminary results from the

unemployment, hours of work, income, and wages.

for 2011 data. A indicates that data are as reported,

very recent censuses could be reflected in timely revi-

Living Standards Measurement Study Surveys (LSMS),

P that data are based on reported or collected informa-

sions if basic data are available, such as population

developed by the World Bank, provide a comprehensive

tion but include an element of staff estimation, and E

by age and sex, as well as the detailed definition of

picture of household welfare and the factors that affect

that data are World Bank staff estimates. • System of

counting, coverage, and completeness. Countries that

it; they typically incorporate data collection at the indi-

trade refers to the United Nations general trade system

hold register-based censuses produce similar census

vidual, household, and community levels. Priority sur-

(G) or special trade system (S). Under the general trade

tables every 5 or 10 years. Germany’s 2001 census is

veys (PS) are a light monitoring survey, designed by the

system goods entering directly for domestic consump-

a register-based test census using a sample of 1.2

World Bank, that collect data from a large number of

tion and goods entered into customs storage are

percent of the population. A rare case, France has been

households cost-effectively and quickly. 1-2-3 (1-2-3)

recorded as imports at arrival. Under the special trade

conducting a rolling census every year since 2004; the

surveys are implemented in three phases and collect

system goods are recorded as imports when declared

1999 general population census was the last to cover

socio­demographic and employment data, data on the

for domestic consumption whether at time of entry or

the entire population simultaneously. • Latest

informal sector, and information on living conditions

World Development Indicators 2013

Front

?

User guide

World view People view People Environment


Primary data documentation notes and household consumption. • Vital registration com-

sourced from the IMF and differ from the Central Sta-

have been revised for 1990 onward; the new base year

plete identifies countries that report at least 90 per-

tistics Organization numbers due to exclusion of the

is 1990. • Croatia. Based on official government sta-

cent complete registries of vital (birth and death) sta-

opium economy. • Angola. Based on IMF data, national

tistics, the new base year for constant price series is

tistics to the United Nations Statistics Division and are

accounts data have been revised for 2000 onward;

2005. • Eritrea. Based on IMF data, national accounts

reported in its Population and Vital Statistics Reports.

the new base year is 2002. • Australia. Value added

data have been revised for 2000 onward; the new base

Countries with complete vital statistics registries may

series data are taken from the United Nations National

year is 2000. • The Gambia. Based on official gov-

have more accurate and more timely demographic

Accounts Main Aggregates, and gross national income

ernment statistics, national accounts data have been

indicators than other countries. • Latest agricultural

is computed using Australian Bureau of Statistics

revised for 2004 onward; the new base year is 2004.

census shows the most recent year in which an agri-

data. • Bhutan. Data were updated recently using the

• Guinea. Based on IMF data, national accounts data

cultural census was conducted and reported to the

government of Bhutan macroeconomic framework.

have been revised for 2000 onward; the new base

Food and Agriculture Organization of the United

• China. National accounts historical data for expendi-

year is 2003. • Hong Kong SAR, China. Agriculture

Nations. • Latest industrial data show the most recent

ture series in constant prices have been revised based

value added includes mining and quarrying. • India.

year for which manufacturing value added data at the

on National Statistics Bureau data not previously avail-

The India Central Statistical Office revised historical

three-digit level of the International Standard Industrial

able. • Democratic Republic of Congo. Based on IMF

data series both current and constant going back to

Classification (revision 2 or 3) are available in the

data, national accounts data have been revised for

1960 with 2004–05 as the base. • Jamaica. Based

United Nations Industrial Development Organization

2000 onward; the new base year is 2000. • Republic

on official government statistics, national accounts

database. • Latest trade data show the most recent

of Congo. Based on IMF data, national accounts data

data have been revised for 2002 onward; the new base year is 2007. • Kiribati. Based on data from the

year for which structure of merchandise trade data from the United Nations Statistics Division’s Commod-

Economies with exceptional reporting periods

ity Trade (Comtrade) database are available. • Latest

Asian Development Bank, national accounts data have been revised for 2005 onward. • Liberia. Based on IMF

water withdrawal data show the most recent year for

Economy

Fiscal year end

Reporting period for national accounts data

which data on freshwater withdrawals have been com-

Afghanistan

Mar. 20

FY

2000 onward; the new base year is 2000. • Malawi.

piled from a variety of sources.

Australia

Jun. 30

FY

Based on IMF data, national accounts data have been

Bangladesh

Jun. 30

FY

revised for 2003 onward; the new base year is 2007.

Exceptional reporting periods

Botswana

Jun. 30

FY

• Malaysia. Based on data from the National Statistics

In most economies the fiscal year is concurrent with

Canada

Mar. 31

CY

Office, national accounts data in current prices have

the calendar year. Exceptions are shown in the table

Egypt, Arab Rep.

Jun. 30

FY

been revised for 2005 onward. • Nicaragua. Based on

at right. The ending date reported here is for the fis-

Ethiopia

Jul. 7

FY

official government statistics, national accounts data

cal year of the central government. Fiscal years for

Gambia, The

Jun. 30

CY

have been revised for 1994 onward; the new base

other levels of government and reporting years for

Haiti

Sep. 30

FY

year is 2006. • Palau. Based on IMF data, national

statistical surveys may differ.

India

Mar. 31

FY

accounts data have been revised for 2007 onward.

Indonesia

Mar. 31

CY

• Rwanda. Based on official government statistics,

designated as either calendar year basis (CY) or fiscal

Iran, Islamic Rep.

Mar. 20

FY

national accounts data have been revised for 1999

year basis (FY). Most economies report their national

Japan

Mar. 31

CY

onward; the new base year is 2006. • Samoa. Based

accounts and balance of payments data using calen-

Kenya

Jun. 30

CY

on IMF data, national accounts data have been revised

dar years, but some use fiscal years. In World Devel-

Kuwait

Jun. 30

CY

for 2007 onward. • Seychelles. Based on official gov-

opment Indicators fiscal year data are assigned to

Lesotho

Mar. 31

CY

ernment statistics, national accounts data have been

the calendar year that contains the larger share of

Malawi

Mar. 31

CY

revised for 1976 onward; the new base year is 2006.

the fiscal year. If a country’s fiscal year ends before

Myanmar

Mar. 31

FY

• Sierra Leone. Based on official government statis-

June 30, data are shown in the first year of the fiscal

Namibia

Mar. 31

CY

tics, national accounts data have been revised for

period; if the fiscal year ends on or after June 30, data

Nepal

Jul. 14

FY

1990 onward; the new base year is 2006. • Syrian

are shown in the second year of the period. Balance

New Zealand

Mar. 31

FY

Arab Republic. Based on data from the Central Bureau

of payments data are reported in World Development

Pakistan

Jun. 30

FY

of Statistics, national accounts data have been revised

Indicators by calendar year.

Puerto Rico

Jun. 30

FY

for 2003 onward. • Togo. Based on IMF data, national

Sierra Leone

Jun. 30

CY

accounts data have been revised for 2000; the new

Revisions to national accounts data

Singapore

Mar. 31

CY

base year is 2000. • Tonga. Based on data from the

National accounts data are revised by national sta-

South Africa

Mar. 31

CY

National Bureau of Statistics, national accounts data

tistical offices when methodologies change or data

Swaziland

Mar. 31

CY

have been revised; the new base year is 2010/11.

sources improve. National accounts data in World

Sweden

Jun. 30

CY

• Tuvalu. Based on IMF data, national accounts data

Thailand

Sep. 30

CY

for 2000 onward have been revised. • United Arab

Uganda

Jun. 30

FY

United States

Sep. 30

CY

Zimbabwe

Jun. 30

CY

The reporting period for national accounts data is

Development Indicators are also revised when data sources change. The following notes, while not comprehensive, provide information on revisions from previous data. • Afghanistan. National accounts data are

Economy

States and markets

Global links

data, national accounts data have been revised for

Emirates. Based on data from the National Bureau of Statistics, national accounts data have been revised for 2001 onward; the new base year is 2007.

Back

World Development Indicators 2013 117


Statistical methods This section describes some of the statistical procedures used in preparing World Development Indicators. It covers the methods employed for calculating regional and income group aggregates and for calculating growth rates, and it describes the World Bank Atlas method for deriving the conversion factor used to estimate gross national income (GNI) and GNI per capita in U.S. dollars. Other statistical procedures and calculations are described in the About the data sections following each table. Aggregation rules Aggregates based on the World Bank’s regional and income classifications of economies appear at the end of the tables, including most of those available online. The 214 economies included in these classifications are shown on the flaps on the front and back covers of the book. Aggregates also contain data for Taiwan, China. Most tables also include the aggregate for the euro area, which includes the member states of the Economic and Monetary Union (EMU) of the European Union that have adopted the euro as their currency: Austria, Belgium, Cyprus, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Malta, Netherlands, Portugal, Slovak Republic, Slovenia, and Spain. Other classifications, such as the European Union, are documented in About the data for the online tables in which they appear. Because of missing data, aggregates for groups of economies should be treated as approximations of unknown totals or average values. The aggregation rules are intended to yield estimates for a consistent set of economies from one period to the next and for all indicators. Small differences between sums of subgroup aggregates and overall totals and averages may occur because of the approximations used. In addition, compilation errors and data reporting practices may cause discrepancies in theoretically identical aggregates such as world exports and world imports. Five methods of aggregation are used in World Development Indicators: • For group and world totals denoted in the tables by a t, missing data are imputed based on the relationship of the sum of available data to the total in

118 

World Development Indicators 2013

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the year of the previous estimate. The imputation process works forward and backward from 2000. Missing values in 2000 are imputed using one of several proxy variables for which complete data are available in that year. The imputed value is calculated so that it (or its proxy) bears the same relationship to the total of available data. Imputed values are usually not calculated if missing data account for more than a third of the total in the benchmark year. The variables used as proxies are GNI in U.S. dollars; total population; exports and imports of goods and services in U.S. dollars; and value added in agriculture, industry, manufacturing, and services in U.S. dollars. Aggregates marked by an sare sums of available data. Missing values are not imputed. Sums are not computed if more than a third of the observations in the series or a proxy for the series are missing in a given year. Aggregates of ratios are denoted by a wwhen calculated as weighted averages of the ratios (using the value of the denominator or, in some cases, another indicator as a weight) and denoted by a u when calculated as unweighted averages. The aggregate ratios are based on available data. Missing values are assumed to have the same average value as the available data. No aggregate is calculated if missing data account for more than a third of the value of weights in the benchmark year. In a few cases the aggregate ratio may be computed as the ratio of group totals after imputing values for missing data according to the above rules for computing totals. Aggregate growth rates are denoted by a wwhen calculated as a weighted average of growth rates. In a few cases growth rates may be computed from time series of group totals. Growth rates are not calculated if more than half the observations in a period are missing. For further discussion of methods of computing growth rates see below. Aggregates denoted by an m are medians of the values shown in the table. No value is shown if more than half the observations for countries with a population of more than 1 million are missing.

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Environment


Exceptions to the rules may occur. Depending on the judgment of World Bank analysts, the aggregates may be based on as little as 50 percent of the available data. In other cases, where missing or excluded values are judged to be small or irrelevant, aggregates are based only on the data shown in the tables. Growth rates Growth rates are calculated as annual averages and represented as percentages. Except where noted, growth rates of values are computed from constant price series. Three principal methods are used to calculate growth rates: least squares, exponential endpoint, and geometric endpoint. Rates of change from one period to the next are calculated as proportional changes from the earlier period. Least squares growth rate. Least squares growth rates are used wherever there is a sufficiently long time series to permit a reliable calculation. No growth rate is calculated if more than half the observations in a period are missing. The least squares growth rate, r, is estimated by fitting a linear regression trend line to the logarithmic annual values of the variable in the relevant period. The regression equation takes the form ln Xt = a + bt which is the logarithmic transformation of the compound growth equation, Xt = Xo (1 + r )t. In this equation X is the variable, t is time, and a = ln Xo and b = ln (1 + r) are parameters to be estimated. If b* is the least squares estimate of b, then the average annual growth rate, r, is obtained as [exp(b*) – 1] and is multiplied by 100 for expression as a percentage. The calculated growth rate is an average rate that is representative of the available observations over the entire period. It does not necessarily match the actual growth rate between any two periods. Exponential growth rate. The growth rate between two points in time for certain demographic indicators,

Economy

States and markets

notably labor force and population, is calculated from the equation r = ln(pn/p0)/n where pn and p0 are the last and first observations in the period, n is the number of years in the period, and ln is the natural logarithm operator. This growth rate is based on a model of continuous, exponential growth between two points in time. It does not take into account the intermediate values of the series. Nor does it correspond to the annual rate of change measured at a one-year interval, which is given by (pn – pn–1)/pn–1. Geometric growth rate. The geometric growth rate is applicable to compound growth over discrete periods, such as the payment and reinvestment of interest or dividends. Although continuous growth, as modeled by the exponential growth rate, may be more realistic, most economic phenomena are measured only at intervals, in which case the compound growth model is appropriate. The average growth rate over n periods is calculated as r = exp[ln(pn/p0)/n] – 1.

World Bank Atlas method In calculating GNI and GNI per capita in U.S. dollars for certain operational purposes, the World Bank uses the Atlas conversion factor. The purpose of the Atlas conversion factor is to reduce the impact of exchange rate fluctuations in the cross-country comparison of national incomes. The Atlas conversion factor for any year is the average of a country’s exchange rate (or alternative conversion factor) for that year and its exchange rates for the two preceding years, adjusted for the difference between the rate of inflation in the country and that in Japan, the United Kingdom, the United States, and the euro area. A country’s inflation rate is measured by the change in its GDP deflator. The inflation rate for Japan, the United Kingdom, the United States, and the euro area, representing

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Statistical methods international inflation, is measured by the change in the “SDR deflator.” (Special drawing rights, or SDRs, are the International Monetary Fund’s unit of account.) The SDR deflator is calculated as a weighted average of these countries’ GDP deflators in SDR terms, the weights being the amount of each country’s currency in one SDR unit. Weights vary over time because both the composition of the SDR and the relative exchange rates for each currency change. The SDR deflator is calculated in SDR terms first and then converted to U.S. dollars using the SDR to dollar Atlas conversion factor. The Atlas conversion factor is then applied to a country’s GNI. The resulting GNI in U.S. dollars is divided by the midyear population to derive GNI per capita. When official exchange rates are deemed to be unreliable or unrepresentative of the effective exchange rate during a period, an alternative estimate of the exchange rate is used in the Atlas formula (see below). The following formulas describe the calculation of the Atlas conversion factor for year t:

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and the calculation of GNI per capita in U.S. dollars for year t: Yt$ = (Yt/Nt)/et* where et* is the Atlas conversion factor (national currency to the U.S. dollar) for year t, et is the average annual exchange rate (national currency to the U.S. dollar) for year t, pt is the GDP deflator for year t, ptS$ is the SDR deflator in U.S. dollar terms for year t, Yt$ is the Atlas GNI per capita in U.S. dollars in year t, Yt is current GNI (local currency) for year t, and Nt is the midyear population for year t. Alternative conversion factors The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. An alternative conversion factor is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate effectively applied to domestic transactions of foreign currencies and traded products. This applies to only a small number of countries, as shown in Primary data documentation. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conversion factors.

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Environment


Credits 1. World view Section 1 was prepared by a team led by Eric Swanson. Eric Swanson wrote the introduction with input from Neil Fantom, Juan Feng, Masako Hiraga, Wendy Huang, Hiroko Maeda, Johan Mistiaen, Vanessa Moreira, Esther Naikal, William Prince, Evis Rucaj, Rubena Sakaj, and Emi Suzuki. Bala Bhaskar Naidu Kalimili coordinated tables 1.1 and 1.6. Masako Hiraga, Hiroko Maeda, Johan Mistiaen, Vanessa Moreira, and Emi Suzuki prepared tables 1.2 and 1.5. Mahyar Eshragh-Tabary, Masako Hiraga, Buyant Erdene Khaltarkhuu, Hiroko Maeda, Vanessa Moreira, and Emi Suzuki prepared table 1.3. Wendy Huang prepared table 1.4 with input from Azita Amjadi. Signe Zeikate of the World Bank’s Economic Policy and Debt Department provided the estimates of debt relief for the Heavily Indebted Poor Countries Debt Initiative and Multilateral Debt Relief Initiative. 2. People Section 2 was prepared by Juan Feng, Masako Hiraga, Hiroko Maeda, Johan Mistiaen, Vanessa Moreira, Emi Suzuki, and Eric Swanson in partnership with the World Bank’s Human Development Network and the Development Research Group in the Development Economics Vice Presidency. Emi Suzuki prepared the demographic estimates and projections. The poverty estimates at national poverty lines were compiled by the Global Poverty Working Group, a team of poverty experts from the Poverty Reduction and Equality Network, the Development Research Group, and the Development Data Group. Shaohua Chen and Prem Sangraula of the World Bank’s Development Research Group prepared the poverty estimates at international poverty lines. Lorenzo Guarcello and Furio Rosati of the Understanding Children’s Work project prepared the data on children at work. Other contributions were provided by Samuel Mills (health); Maddalena Honorati, Montserrat Pallares-Miralles, and Claudia Rodríguez (vulnerability and security); Theodoor Sparreboom and Alan Wittrup of the International Labour Organization (labor force); Amélie Gagnon, Said Ould Voffal, and Weixin Lu of the United Nations Educational, Scientific and Cultural Organization Institute for

Economy

States and markets

Statistics (education and literacy); the World Health Organization Chandika Indikadahena (health expenditure), Monika Bloessner and Mercedes de Onis (malnutrition and overweight), Teena Kunjumen (health workers), Jessica Ho (hospital beds), Rifat Hossain (water and sanitation), Luz Maria de Regil (anemia), Hazim Timimi (tuberculosis), and Lori Marie Newman (syphilis); Leonor Guariguata of the International Diabetes Federation (diabetes); Mary Mahy of the Joint United Nations Programme on HIV/AIDS (HIV/AIDS); and Colleen Murray of the United Nations Children’s Fund (health). Eric Swanson provided comments and suggestions on the introduction and at all stages of production. 3. Environment Section 3 was prepared by Mahyar Eshragh-Tabary in partnership with the Agriculture and Environmental Services Department of the Sustainable Development Network Vice Presidency of the World Bank. Mahyar Eshragh-Tabary wrote the introduction with suggestions from Eric Swanson. Other contributors include Esther G. Naikal and Karen Treanton of the International Energy Agency, Gerhard Metchies and Armin Wagner of German International Cooperation, Craig Hilton-Taylor and Caroline Pollock of the International Union for Conservation of Nature, and Cristian Gonzalez of the International Road Federation. The World Bank’s Agriculture and Environmental Services Department devoted generous staff resources. 4. Economy Section 4 was prepared by Bala Bhaskar Naidu Kalimili in close collaboration with the Sustainable Development and Economic Data Team of the World Bank’s Development Data Group and with suggestions from Liu Cui and William Prince. Bala Bhaskar Naidu Kalimili wrote the introduction with suggestions from Eric Swanson. The highlights section was prepared by Bala Bhaskar Naidu Kalimili, Maurice Nsabimana, and Olga Victorovna Vybornaia. The national accounts data for low- and middle-income economies were gathered by the World Bank’s regional staff through the annual Unified Survey. Federico M. Escaler, Mahyar Eshragh-

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Credits Tabary, Bala Bhaskar Naidu Kalimili, Buyant Erdene Khaltarkhuu, Maurice Nsabimana, and Olga Victorovna Vybornaia updated, estimated, and validated the databases for national accounts. Esther G. Naikal prepared adjusted savings and adjusted income data. Azita Amjadi contributed trade data from the World Integrated Trade Solution. The team is grateful to Eurostat, the International Monetary Fund, the Organisation for Economic Co-operation and Development, the United Nations Industrial Development Organization, and the World Trade Organization for access to their databases. 5. States and markets Section 5 was prepared by Federico Escaler and Buyant Erdene Khaltarkhuu in partnership with the World Bank’s Financial and Private Sector Development Network, Poverty Reduction and Economic Management Network, and Sustainable Development Network; the International Finance Corporation; and external partners. Buyant Erdene Khaltarkhuu wrote the introduction with input from Eric Swanson. Other contributors include Alexander Nicholas Jett (privatization and infrastructure projects); Leora Klapper (business registration); Federica Saliola and Joshua Wimpey (Enterprise Surveys); Carolin Geginat and Frederic Meunier (Doing Business); Alka Banerjee, Trisha Malinky, and Michael Orzano (Standard & Poor’s global stock market indexes); Gary Milante and Kenneth Anya (fragile situations); Satish Mannan (public policies and institutions); James Hackett of the International Institute for Strategic Studies (military personnel); Sam Perlo-Freeman of the Stockholm International Peace Research Institute (military expenditures and arms transfers); Christian Gonzalez of the International Road Federation, Zubair Anwar and Narjess Teyssier of the International Civil Aviation Organization, and Marc Juhel and Hélène Stephan (transport); Vincent Valentine of the United Nations Conference on Trade and Development (ports); Azita Amjadi (high-tech exports); Vanessa Grey, Esperanza Magpantay, and Susan Teltscher of the International Telecommunication Union; Torbjörn Fredriksson and Diana Korka of the United Nations Conference on

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Trade and Development (information and communication technology goods trade); Martin Schaaper of the United Nations Educational, Scientific and Cultural Organization Institute for Statistics (research and development, researchers, and technicians); and Ryan Lamb of the World Intellectual Property Organization (patents and trademarks). 6. Global links Section 6 was prepared by Wendy Huang with input from Evis Rucaj and Rubena Sukaj and in partnership with the Financial Data Team of the World Bank’s Development Data Group, Development Research Group (trade), Development Prospects Group (commodity prices and remittances), International Trade Department (trade facilitation), and external partners. Wendy Huang and Evis Rucaj wrote the introduction, with substantial input from Eric Swanson. Azita Amjadi (trade and tariffs) and Rubena Sukaj (external debt and financial data) provided substantial input on the data and tables. Other contributors include Frédéric Docquier (emigration rates); Flavine Creppy and Yumiko Mochizuki of the United Nations Conference on Trade and Development and Mondher Mimouni of the International Trade Centre (trade); Cristina Savescu (commodity prices); Jeff Reynolds and Joseph Siegel of DHL (freight costs); Yasmin Ahmad and Elena Bernaldo of the Organisation for Economic Co-operation and Development (aid); Ibrahim Levent and Maryna Taran (external debt); Gemechu Ayana Aga and Ani Rudra Silwal (remittances); and Teresa Ciller of the World Tourism Organization (tourism). Ramgopal Erabelly, Shelley Fu, and William Prince provided technical assistance. Other parts of the book Jeff Lecksell of the World Bank’s Map Design Unit coordinated preparation of the maps on the inside covers. Alison Kwong and William Prince prepared User guide and the lists of online tables and indicators for each section. Eric Swanson wrote Statistical methods, with input from William Prince. Federico Escaler and Leila Rafei prepared Primary data documentation. Partners was prepared by Alison Kwong.

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Database management William Prince coordinated management of the World Development Indicators database, with assistance from Liu Cui and Shelley Fu in the Data Administration and Quality Team. Operation of the database management system was made possible by Ramgopal Erabelly in the Data and Information Systems Team under the leadership of Reza Farivari. Design, production, and editing Azita Amjadi and Alison Kwong coordinated all stages of production with Communications Development Incorporated, which provided overall design direction, editing, and layout, led by Meta de Coquereaumont, Jack Harlow, Bruce Ross-Larson, and Christopher Trott. Elaine Wilson created the cover and graphics and typeset the book. Peter Grundy, of Peter Grundy Art & Design, and Diane Broadley, of Broadley Design, designed the report. Administrative assistance, office technology, and systems development support Elysee Kiti provided administrative assistance. Jean-Pierre Djomalieu, Gytis Kanchas, and Nacer Megherbi provided information technology support. Ugendran Machakkalai, Shanmugam Natarajan, Atsushi Shimo, and Malarvizhi Veerappan provided software support on the Development Data Platform application. Publishing and dissemination The Office of the Publisher, under the direction of Carlos Rossel, provided assistance throughout the production process. Denise Bergeron, Stephen McGroarty, Nora Ridolfi, and Janice Tuten coordinated printing, marketing, and distribution. Merrell TuckPrimdahl of the Development Economics Vice President’s Office managed the communications strategy.

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World Development Indicators mobile applications Software preparation and testing were managed by Shelley Fu with assistance from Prashant Chaudhari, Ying Chi, Liu Cui, Ghislaine Delaine, Neil Fantom, Ramgopal Erabelly, Federico Escaler, Buyant Erdene Khaltarkhuu, Sup Lee, Maurice Nsabimana, Parastoo Oloumi, Beatriz Prieto Oramas, William Prince, Virginia Romand, Jomo Tariku, Malarvizhi Veerappan, and Vera Wen. Systems development was undertaken in the Data and Information Systems Team led by Reza Farivari. William Prince provided data quality assurance. Online access Coordination of the presentation of the WDI online, through the Open Data website, the World Databank application, the new table browser application, and the Application Programming Interface, was provided by Neil Fantom and Soong Sup Lee. Development and maintenance of the website were managed by a team led by Azita Amjadi and including Alison Kwong, George Gongadze, Timothy Herzog, Jeffrey McCoy, and Jomo Tariku. Systems development was managed by a team led by Reza Farivari, with project management provided by Malarvizhi Veerappan. Design, programming, and testing were carried out by Ying Chi, Shelley Fu, Siddhesh Kaushik, Ugendran Machakkalai, Nacer Megherbi, Shanmugam Natarajan, Parastoo Oloumi, Manish Rathore, Ashish B. Shah, Atsushi Shimo, Maryna Taran, and Jomo Tariku. Liu Cui and William Prince coordinated production and provided data quality assurance. Multilingual translations of online applications were provided by a team led by Jim Rosenberg in the World Bank’s External Affairs department. Client feedback The team is grateful to the many people who have taken the time to provide feedback and suggestions, which have helped improve this year’s edition. Please contact us at data@worldbank.org.

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ECO-AUDIT

Environmental Benefits Statement The World Bank is committed to preserving endangered forests and natural resources. The Office of the Publisher has chosen to print World Development Indicators 2013 on recycled paper with 50 percent postconsumer fiber in accordance with the recommended standards for paper usage set by the Green Press Initiative, a nonprofit program supporting publishers in using fiber that is not sourced from endangered forests. For more information, visit www.greenpressinitiative.org.

Saved: • 25 trees • 11 million British Thermal Units of total energy • 2,172 pounds of net greenhouse gases • 11,779 gallons of waste water • 789 pounds of solid waste


Classified according to World Bank analytical grouping

The world by region

Low- and middle-income economies

East Asia and Pacific

Middle East and North Africa

High-income economies

Europe and Central Asia

South Asia

OECD

Latin America and the Caribbean

Sub-Saharan Africa

Other

No data

Greenland (Den)

Iceland

Isle of Man (UK)

Canada

Ireland Channel Islands (UK)

Luxembourg Liechtenstein Switzerland Andorra United States

Norway

Faeroe Islands (Den)

The Netherlands

Sweden

Spain Monaco Tunisia

Cayman Is.(UK)

Costa Rica

Jordan

Libya

Arab Rep. of Egypt

Cape Verde

Mali

Senegal The Gambia Guinea-Bissau Guinea R.B. de Venezuela

Guyana Suriname

Sierra Leone Liberia

French Guiana (Fr)

Burkina Faso

Niger

Benin

Côte Ghana d’Ivoire

Eritrea

Sudan

Cameroon

Central African Republic

Congo

Malawi

Zambia Bolivia Zimbabwe

Tonga

Namibia Paraguay Germany

St. Martin (Fr) St. Maarten (Neth)

Antigua and Barbuda St. Kitts and Nevis

Curaçao (Neth)

St. Vincent and the Grenadines

Dominica St. Lucia Barbados Grenada

R.B. de Venezuela

Argentina

Trinidad and Tobago

Poland

Czech Republic Ukraine Slovak Republic Austria

Guadeloupe (Fr)

Martinique (Fr) Aruba (Neth)

Chile

Uruguay

Lao P.D.R.

N. Mariana Islands (US) Guam (US)

Philippines

Federated States of Micronesia

Brunei Darussalam Malaysia

Marshall Islands

Palau

Maldives Nauru

Singapore

Botswana

Comoros

Solomon Islands

Papua New Guinea

Indonesia

Mayotte (Fr)

Madagascar

Tuvalu

Vanuatu

Fiji

Mauritius Réunion (Fr)

Australia

New Caledonia (Fr)

Lesotho

New Zealand

Hungary

Slovenia Croatia

Romania

Bosnia and Herzegovina San Marino Italy Montenegro Vatican City

South Africa

Kiribati

Seychelles

Mozambique

Swaziland

U.S. Virgin Islands (US)

Sri Lanka Somalia

Angola

Puerto Rico (US)

Myanmar

Timor-Leste

French Polynesia (Fr) American Samoa (US)

India

Vietnam Cambodia

Brazil

Peru

Bangladesh

Thailand

Kenya

Rwanda Dem.Rep.of Burundi Congo Tanzania

Japan

Bhutan

Nepal

Rep. of Yemen

Ethiopia

South Sudan Uganda

Gabon

Kiribati

Dominican Republic

Pakistan

United Arab Emirates Oman

Rep.of Korea

China

Afghanistan

Bahrain Qatar Saudi Arabia

Dem.People’s Rep.of Korea

Tajikistan

Djibouti Nigeria

Togo Equatorial Guinea São Tomé and Príncipe

Ecuador

Chad

Turkmenistan

Islamic Rep. of Iran Kuwait

Iraq

Mongolia Kyrgyz Rep.

Uzbekistan

Azerbaijan

Mauritania

Colombia

Fiji

West Bank and Gaza

Western Sahara

Haiti

Panama

Samoa

Syrian Arab Rep.

Turks and Caicos Is. (UK)

Cuba Belize Jamaica Guatemala Honduras El Salvador Nicaragua

Georgia Armenia

Cyprus Lebanon Israel

Malta

Morocco Algeria

Mexico

Kazakhstan

Turkey

Greece

Gibraltar (UK)

The Bahamas

Russian Federation

Bulgaria

Portugal Bermuda (UK)

Finland

Estonia Denmark Russian Latvia Fed. Lithuania United Belarus Germany Poland Kingdom Belgium Ukraine Moldova Romania France Italy

Serbia

Kosovo Bulgaria FYR Macedonia Albania Greece

Antarctica

IBRD 39818 MARCH 2013


World Development Indicators 2013  

A new look and new ways to access the world’s premier source of development data. Looking for accurate, up-to-date data on development issue...

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